diff --git a/Merge/back_jwlll/README.md b/Merge/back_jwlll/README.md
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+# JWLLL 搜索推荐算法服务
+
+这个模块包含了 JWLLL 的搜索推荐算法服务，提供以下功能：
+
+## 主要功能
+
+1. **智能搜索**
+   - 支持中文分词
+   - 拼音搜索
+   - 语义关联搜索
+   - TF-IDF 向量相似度
+   - Word2Vec 语义扩展
+
+2. **推荐算法**
+   - 基于标签的内容推荐
+   - 协同过滤推荐
+   - 个性化推荐
+
+3. **帖子管理**
+   - 帖子详情查看
+   - 点赞/取消点赞
+   - 评论功能
+   - 帖子上传
+
+## API 接口
+
+- `POST /search` - 搜索内容
+- `GET /user_tags` - 获取用户标签
+- `POST /recommend_tags` - 标签推荐
+- `POST /user_based_recommend` - 协同过滤推荐
+- `GET /post/<id>` - 获取帖子详情
+- `POST /like` - 点赞帖子
+- `POST /unlike` - 取消点赞
+- `POST /comment` - 添加评论
+- `GET /comments/<post_id>` - 获取评论
+- `POST /upload` - 上传帖子
+
+## 部署说明
+
+1. 确保安装了所有依赖：
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. 配置数据库连接：
+   - 修改 `config.py` 中的数据库配置
+
+3. 启动服务：
+   ```bash
+   python app.py
+   ```
+
+4. 服务将在 http://127.0.0.1:5000 启动
+
+## 配置文件
+
+- `semantic_config.json` - 语义映射配置
+- `models/chinese_word2vec.bin` - Word2Vec 模型文件（可选）
+
+## 注意事项
+
+- Word2Vec 模型文件较大，如果没有可以禁用该功能
+- 确保数据库中有测试数据
+- 默认用户ID为 '3'，请确保数据库中存在该用户
diff --git a/Merge/back_jwlll/app.py b/Merge/back_jwlll/app.py
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+# main_online.py
+# 搜索推荐算法服务的主入口
+
+import json
+import numpy as np
+import difflib
+from flask import Flask, request, jsonify, Response
+import pymysql
+import jieba
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import pypinyin
+from flask_cors import CORS
+import re
+import Levenshtein
+import os
+import logging
+
+# 设置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger("allpt-search")
+
+# 导入Word2Vec辅助模块
+try:
+    from word2vec_helper import get_word2vec_helper, expand_query, get_similar_words
+    WORD2VEC_ENABLED = True
+    logger.info("Word2Vec模块已加载")
+except ImportError as e:
+    logger.warning(f"Word2Vec模块加载失败: {e}，将使用传统搜索")
+    WORD2VEC_ENABLED = False
+
+# 数据库配置
+DB_CONFIG = {
+    "host": "10.126.59.25",
+    "port": 3306,
+    "user": "root",
+    "password": "123456",
+    "database": "redbook",
+    "charset": "utf8mb4"
+}
+
+def get_db_conn():
+    return pymysql.connect(**DB_CONFIG)
+
+def get_pinyin(text):
+    # 返回字符串的全拼音（不带声调，全部小写），支持英文直接返回
+    if not text:
+        return ""
+    import re
+    # 如果全是英文，直接返回小写
+    if re.fullmatch(r'[a-zA-Z]+', text):
+        return text.lower()
+    return ''.join([p[0] for p in pypinyin.pinyin(text, style=pypinyin.NORMAL)])
+
+def get_pinyin_initials(text):
+    # 返回字符串的首字母拼音（全部小写），支持英文直接返回
+    if not text:
+        return ""
+    import re
+    if re.fullmatch(r'[a-zA-Z]+', text):
+        return text.lower()
+    return ''.join([p[0][0] for p in pypinyin.pinyin(text, style=pypinyin.NORMAL)])
+
+# 新增词语相似度计算函数
+def word_similarity(word1, word2):
+    """计算两个词的相似度，支持拼音匹配"""
+    # 直接匹配
+    if word1 == word2:
+        return 1.0
+    
+    # 拼音匹配
+    if get_pinyin(word1) == get_pinyin(word2):
+        return 0.9
+    
+    # 拼音首字母匹配
+    if get_pinyin_initials(word1) == get_pinyin_initials(word2):
+        return 0.7
+    
+    # 字符串相似度
+    return difflib.SequenceMatcher(None, word1, word2).ratio()
+
+def semantic_title_similarity(query, title):
+    """计算查询词与标题的语义相似度"""
+    # 分词
+    query_words = list(jieba.cut(query))
+    title_words = list(jieba.cut(title))
+    
+    if not query_words or not title_words:
+        return 0.0
+    
+    # 计算每个查询词与标题词的最大相似度
+    max_similarities = []
+    key_matches = 0  # 关键词精确匹配数量
+    
+    for q_word in query_words:
+        if len(q_word.strip()) <= 1:  # 忽略单字，减少噪音
+            continue
+            
+        word_sims = [word_similarity(q_word, t_word) for t_word in title_words]
+        if word_sims:
+            max_sim = max(word_sims)
+            max_similarities.append(max_sim)
+            if max_sim > 0.85:  # 认为是关键词匹配
+                key_matches += 1
+    
+    if not max_similarities:
+        return 0.0
+    
+    # 计算平均相似度
+    avg_sim = sum(max_similarities) / len(max_similarities)
+    
+    # 权重计算: 平均相似度占70%，关键词匹配率占30%
+    key_match_ratio = key_matches / len(query_words) if query_words else 0
+    
+    # 标题中包含完整查询短语时给予额外加分
+    exact_bonus = 0.3 if query in title else 0
+    
+    return 0.7 * avg_sim + 0.3 * key_match_ratio + exact_bonus
+
+# 添加语义关联词典，用于增强搜索能力
+def load_semantic_mappings():
+    """
+    加载语义关联映射表，用于增强搜索语义理解
+    返回包含语义映射关系的字典
+    """
+    # 初始化空字典，所有映射将从配置文件加载
+    mappings = {}
+    
+    # 从配置文件加载映射
+    try:
+        config_path = os.path.join(os.path.dirname(__file__), "semantic_config.json")
+        if os.path.exists(config_path):
+            with open(config_path, 'r', encoding='utf-8') as f:
+                mappings = json.load(f)
+            logger.info(f"已从配置文件加载 {len(mappings)} 个语义映射")
+        else:
+            logger.warning(f"语义配置文件不存在: {config_path}")
+    except Exception as e:
+        logger.error(f"加载语义配置文件失败: {e}")
+    
+    return mappings
+
+# 初始化语义映射
+SEMANTIC_MAPPINGS = load_semantic_mappings()
+
+def expand_search_keywords(keyword):
+    """
+    扩展搜索关键词，增加语义关联词
+    """
+    expanded = [keyword]
+    
+    # 分词处理
+    words = list(jieba.cut(keyword))
+    logger.info(f"关键词 '{keyword}' 分词结果: {words}")  # 记录分词结果
+    
+    # 分别对每个分词进行语义扩展
+    for word in words:
+        if word in SEMANTIC_MAPPINGS:
+            # 添加语义关联词
+            mapped_words = SEMANTIC_MAPPINGS[word]
+            expanded.extend(mapped_words)
+            logger.info(f"语义映射: '{word}' -> {mapped_words}")
+            
+            # 移除所有特殊处理部分
+            # 不再对任何特定关键词如"越狱"进行特殊处理
+    
+    # Word2Vec扩展 - 如果可用，对分词结果进行Word2Vec扩展
+    if WORD2VEC_ENABLED:
+        try:
+            # 使用单独的变量记录原始扩展结果，方便记录日志
+            original_expanded = set(expanded)
+            
+            # 首先尝试对整个关键词进行扩展
+            w2v_expanded = set()
+            similar_words = get_similar_words(keyword, topn=3, min_similarity=0.6)
+            w2v_expanded.update(similar_words)
+            
+            # 然后对较长的分词进行扩展
+            for word in words:
+                if len(word) > 1:  # 忽略单字
+                    similar_words = get_similar_words(word, topn=2, min_similarity=0.65)
+                    w2v_expanded.update(similar_words)
+            
+            # 合并结果
+            expanded.extend(w2v_expanded)
+            
+            # 记录日志
+            if w2v_expanded:
+                logger.info(f"Word2Vec扩展: {keyword} -> {list(w2v_expanded)}")
+        except Exception as e:
+            # 出错时记录但不中断搜索流程
+            logger.error(f"Word2Vec扩展失败: {e}")
+            logger.info("将仅使用配置文件中的语义映射")
+    
+    # 去重
+    return list(set(expanded))
+
+# 替换原有的calculate_keyword_relevance函数，采用更通用的相关性算法
+def calculate_keyword_relevance(keyword, item):
+    """计算搜索关键词与条目的相关性得分"""
+    title = item.get('title', '')
+    description = item.get('description', '') or ''
+    tags = item.get('tags', '') or ''
+    category = item.get('category', '') or ''  # 添加category字段
+    
+    # 初始化得分
+    score = 0
+    
+    # 1. 精确匹配（最高优先级）
+    if keyword.lower() == title.lower():
+        return 15.0  # 完全匹配给予最高分
+    
+    # 2. 标题中精确词匹配
+    title_words = re.findall(r'\b\w+\b', title.lower())
+    if keyword.lower() in title_words:
+        score += 10.0  # 作为独立词完全匹配
+    
+    # 3. 标题包含关键词（部分匹配）
+    elif keyword.lower() in title.lower():
+        # 计算关键词所占标题比例
+        match_ratio = len(keyword) / len(title)
+        if match_ratio > 0.5:  # 关键词占标题很大比例
+            score += 8.0
+        else:
+            score += 5.0
+    
+    # 4. 标题分词匹配
+    keyword_words = list(jieba.cut(keyword))
+    title_jieba_words = list(jieba.cut(title))
+    
+    matched_words = 0
+    for k_word in keyword_words:
+        if len(k_word) > 1:  # 忽略单字
+            if k_word in title_jieba_words:
+                matched_words += 1
+            else:
+                # 拼音匹配
+                k_pinyin = get_pinyin(k_word)
+                for t_word in title_jieba_words:
+                    if get_pinyin(t_word) == k_pinyin:
+                        matched_words += 0.8
+                        break
+    
+    if len(keyword_words) > 0:
+        word_match_ratio = matched_words / len(keyword_words)
+        score += 3.0 * word_match_ratio
+    
+    # 5. 拼音相似度
+    keyword_pinyin = get_pinyin(keyword)
+    title_pinyin = get_pinyin(title)
+    
+    if keyword_pinyin == title_pinyin:
+        score += 3.5
+    elif keyword_pinyin in title_pinyin:
+        # 计算拼音在标题中的位置影响
+        pos = title_pinyin.find(keyword_pinyin)
+        if pos == 0:  # 出现在开头
+            score += 3.0
+        else:
+            score += 2.0
+    
+    # 6. 编辑距离相似度
+    try:
+        edit_distance = Levenshtein.distance(keyword.lower(), title.lower())
+        max_len = max(len(keyword), len(title))
+        if max_len > 0:
+            similarity = 1 - (edit_distance / max_len)
+            if similarity > 0.7:
+                score += 1.5 * similarity
+    except:
+        similarity = difflib.SequenceMatcher(None, keyword.lower(), title.lower()).ratio()
+        if similarity > 0.7:
+            score += 1.5 * similarity
+    
+    # 7. 中文字符重叠检测 - 修改为仅当重叠2个以上汉字或占比超过40%时才计分
+    if re.search(r'[\u4e00-\u9fff]', keyword) and re.search(r'[\u4e00-\u9fff]', title):
+        cn_chars_keyword = set(re.findall(r'[\u4e00-\u9fff]', keyword))
+        cn_chars_title = set(re.findall(r'[\u4e00-\u9fff]', title))
+        
+        # 计算重叠的汉字集合
+        overlapped_chars = cn_chars_keyword & cn_chars_title
+        
+        # 仅当重叠汉字数量大于1且占比超过阈值时才计分
+        if len(overlapped_chars) > 1 and len(cn_chars_keyword) > 0:
+            overlap_ratio = len(overlapped_chars) / len(cn_chars_keyword)
+            # 增加重叠比例的阈值要求，防止单个汉字导致的误匹配
+            if overlap_ratio >= 0.4 or len(overlapped_chars) >= 3:
+                score += 2.0 * overlap_ratio
+            # 对于非常低的重叠度，不加分，避免无关内容干扰
+        
+        # 记录日志，帮助调试特定案例
+        if keyword == "明日方舟" and "白日梦想家" in title:
+            logger.info(f"'明日方舟'与'{title}'的汉字重叠: {overlapped_chars}, 重叠比例: {len(overlapped_chars)/len(cn_chars_keyword) if cn_chars_keyword else 0}")
+    
+    # 8. 序列资源检测（如"功夫熊猫2"是"功夫熊猫"的系列）
+    base_title_match = re.match(r'(.*?)([0-9]+|[一二三四五六七八九十]|：|\:|\s+[0-9]+)', title)
+    if base_title_match:
+        base_title = base_title_match.group(1).strip()
+        if keyword.lower() == base_title.lower():
+            score += 2.0
+    
+    # 9. 标签和描述匹配（增加权重）
+    if tags:
+        tags_list = tags.split(',')
+        if keyword in tags_list:
+            score += 1.5  # 提高标签匹配的权重
+        elif any(keyword.lower() in tag.lower() for tag in tags_list):
+            score += 1.0  # 提高部分匹配的权重
+    
+    # 描述匹配增强
+    if keyword.lower() in description.lower():
+        score += 1.5  # 提高描述匹配的权重
+        
+        # 检查关键词在描述中的位置和上下文
+        pos = description.lower().find(keyword.lower())
+        if pos >= 0 and pos < len(description) / 3:
+            # 关键词出现在描述前1/3部分，可能更重要
+            score += 0.5
+    
+    # 考虑分词匹配描述
+    keyword_words = list(jieba.cut(keyword))
+    description_words = list(jieba.cut(description))
+    matched_desc_words = 0
+    for k_word in keyword_words:
+        if len(k_word) > 1 and k_word in description_words:
+            matched_desc_words += 1
+    
+    if len(keyword_words) > 0:
+        desc_match_ratio = matched_desc_words / len(keyword_words)
+        score += 1.0 * desc_match_ratio
+    
+    # 分类匹配
+    if keyword.lower() in category.lower():
+        score += 1.0
+    
+    # 添加语义关联匹配得分
+    # 扩展关键词进行匹配
+    expanded_keywords = expand_search_keywords(keyword)
+    # 检测标题是否包含语义相关词
+    for exp_keyword in expanded_keywords:
+        if exp_keyword != keyword and exp_keyword in title:  # 避免重复计算原关键词
+            score += 1.5  # 一般语义关联
+    
+    return score
+
+# 创建Flask应用
+app = Flask(__name__)
+CORS(app)  # 允许所有跨域请求
+
+# 添加init_word2vec函数
+def init_word2vec():
+    """初始化Word2Vec模型"""
+    try:
+        helper = get_word2vec_helper()
+        if helper.initialized:
+            logger.info(f"Word2Vec模型已成功加载，词汇量: {len(helper.model.index_to_key)}, 向量维度: {helper.model.vector_size}")
+        else:
+            if helper.load_model():
+                logger.info(f"Word2Vec模型加载成功，词汇量: {len(helper.model.index_to_key)}, 向量维度: {helper.model.vector_size}")
+            else:
+                logger.error("Word2Vec模型加载失败")
+    except Exception as e:
+        logger.error(f"初始化Word2Vec出错: {e}")
+
+# 新的初始化方式:
+def initialize_app():
+    """应用初始化函数，替代before_first_request装饰器"""
+    # 修正：使用正确的函数名
+    # 原代码: init_semantic_mapping()
+    # 修正为使用已定义的函数名
+    global SEMANTIC_MAPPINGS
+    SEMANTIC_MAPPINGS = load_semantic_mappings()  # 更新全局语义映射变量
+    
+    if WORD2VEC_ENABLED:
+        init_word2vec()  # 现在这个函数已经定义了
+
+# 在启动应用之前调用初始化函数
+initialize_app()
+
+# 测试路由
+@app.route('/test', methods=['GET'])
+def test():
+    import datetime
+    return jsonify({"message": "服务器正常运行", "timestamp": str(datetime.datetime.now())})
+
+# 获取单个帖子详情的API
+@app.route('/post/<int:post_id>', methods=['GET'])
+def get_post_detail(post_id):
+    """
+    获取单个帖子详情
+    """
+    logger.info(f"接收到获取帖子详情请求，post_id: {post_id}")
+    conn = get_db_conn()
+    try:
+        with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+            # 联表查询帖子详情，获取分类名和type
+            query = """
+            SELECT 
+                p.id,
+                p.title,
+                p.content,
+                p.heat,
+                p.created_at as create_time,
+                p.updated_at as last_active,
+                p.status,
+                p.type,
+                tp.name as category
+            FROM posts p 
+            LEFT JOIN topics tp ON p.topic_id = tp.id
+            WHERE p.id = %s
+            """
+            logger.info(f"执行查询: {query} with post_id: {post_id}")
+            cursor.execute(query, (post_id,))
+            post = cursor.fetchone()
+            
+            logger.info(f"查询结果: {post}")
+            
+            if not post:
+                logger.warning(f"帖子不存在，post_id: {post_id}")
+                return jsonify({"error": "帖子不存在"}), 404
+            
+            # 设置默认值
+            post['tags'] = []
+            post['author'] = '匿名用户'
+            if not post.get('category'):
+                post['category'] = '未分类'
+            if not post.get('type'):
+                post['type'] = 'text'
+            # 格式化时间
+            if post['create_time']:
+                post['create_time'] = post['create_time'].strftime('%Y-%m-%d %H:%M:%S')
+            if post['last_active']:
+                post['last_active'] = post['last_active'].strftime('%Y-%m-%d %H:%M:%S')
+            
+            logger.info(f"返回帖子详情: {post}")
+            return Response(json.dumps(post, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    except Exception as e:
+        logger.error(f"获取帖子详情失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return jsonify({"error": "服务器内部错误"}), 500
+    finally:
+        conn.close()
+
+# 搜索功能的API
+@app.route('/search', methods=['POST'])
+def search():
+    """
+    搜索功能API
+    请求格式：{
+        "keyword": "关键词",
+        "sort_by": "downloads" | "downloads_asc" | "newest" | "oldest" | "similarity" | "title_asc" | "title_desc",
+        "category": "可选，分类名",
+        "search_mode": "title" | "title_desc" | "tags" | "all"  # 可选，默认"title",
+        "tags": ["标签1", "标签2"]  # 可选，支持传递多个标签
+    }
+    """
+    if request.content_type != 'application/json':
+        return jsonify({"error": "Content-Type must be application/json"}), 415
+
+    data = request.get_json()
+    keyword = data.get("keyword", "").strip()
+    sort_by = data.get("sort_by", "similarity")  # 默认按相似度排序
+    category = data.get("category", None)
+    search_mode = data.get("search_mode", "title")
+    tags = data.get("tags", None)  # 支持传递多个标签
+
+    # 校验参数 - 不管什么模式都要求关键词
+    if not (1 <= len(keyword) <= 20):
+        return jsonify({"error": "请输入1-20个字符"}), 400
+
+    # 第一阶段：数据库查询获取候选集
+    results = []
+    conn = get_db_conn()
+    try:
+        with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+            # 首先尝试查询完全匹配的结果
+            exact_query = f"""
+                SELECT id, title, topic_id, heat, created_at, content
+                FROM posts
+                WHERE title = %s
+            """
+            cursor.execute(exact_query, (keyword,))
+            exact_matches = cursor.fetchall() or []  # 确保返回列表而非元组
+            
+            # 扩展关键词，增加语义关联词
+            expanded_keywords = expand_search_keywords(keyword)
+            logger.info(f"扩展后的关键词: {expanded_keywords}")  # 调试信息
+            
+            # 构建查询条件
+            conditions = []
+            params = []
+            
+            # 标题匹配 - 所有搜索模式都匹配title
+            conditions.append("title LIKE %s")
+            params.append(f"%{keyword}%")
+            
+            # 为扩展关键词添加标题匹配条件
+            for exp_keyword in expanded_keywords:
+                if exp_keyword != keyword:  # 避免重复原关键词
+                    conditions.append("title LIKE %s")
+                    params.append(f"%{exp_keyword}%")
+            
+            # 描述匹配
+            if search_mode in ["title_desc", "all"]:
+                # 原始关键词匹配描述
+                conditions.append("content LIKE %s")
+                params.append(f"%{keyword}%")
+                
+                # 扩展关键词匹配描述
+                for exp_keyword in expanded_keywords:
+                    if exp_keyword != keyword:
+                        conditions.append("content LIKE %s")
+                        params.append(f"%{exp_keyword}%")
+            
+            # 标签匹配
+            # 暂不处理，后续join实现
+            
+            # 分类匹配 - 仅在all模式下
+            if search_mode == "all":
+                # 原始关键词匹配分类
+                conditions.append("topic_id LIKE %s")
+                params.append(f"%{keyword}%")
+                
+                # 扩展关键词匹配分类
+                for exp_keyword in expanded_keywords:
+                    if exp_keyword != keyword:
+                        conditions.append("topic_id LIKE %s")
+                        params.append(f"%{exp_keyword}%")
+            
+            # 构建SQL查询
+            if conditions:
+                where_clause = " OR ".join(conditions)
+                logger.info(f"搜索条件: {where_clause}")
+                logger.info(f"参数列表: {params}")
+                
+                if category:
+                    where_clause = f"({where_clause}) AND topic_id=%s"
+                    params.append(category)
+                
+                sql = f"""
+                    SELECT p.id, p.title, tp.name as category, p.heat, p.created_at, p.content,
+                        GROUP_CONCAT(t.name) as tags
+                    FROM posts p
+                    LEFT JOIN post_tags pt ON p.id = pt.post_id
+                    LEFT JOIN tags t ON pt.tag_id = t.id
+                    LEFT JOIN topics tp ON p.topic_id = tp.id
+                    WHERE {where_clause}
+                    GROUP BY p.id
+                    LIMIT 500
+                """
+                
+                cursor.execute(sql, params)
+                expanded_results = cursor.fetchall()
+                logger.info(f"数据库返回记录数: {len(expanded_results) if expanded_results else 0}")
+            else:
+                expanded_results = []
+
+            # 如果扩展查询和精确匹配都没有结果，获取全部记录进行相关性计算
+            if not expanded_results and not exact_matches:
+                sql = "SELECT p.id, p.title, tp.name as category, p.heat, p.created_at, p.content, GROUP_CONCAT(t.name) as tags FROM posts p LEFT JOIN post_tags pt ON p.id = pt.post_id LEFT JOIN tags t ON pt.tag_id = t.id LEFT JOIN topics tp ON p.topic_id = tp.id"
+                if category:
+                    sql += " WHERE p.topic_id=%s"
+                    category_params = [category]
+                    cursor.execute(sql + " GROUP BY p.id", category_params)
+                else:
+                    cursor.execute(sql + " GROUP BY p.id")
+                
+                all_results = cursor.fetchall() or []  # 确保返回列表
+            else:
+                if isinstance(exact_matches, tuple):
+                    exact_matches = list(exact_matches)
+                if isinstance(expanded_results, tuple):
+                    expanded_results = list(expanded_results)
+                all_results = expanded_results + exact_matches
+            
+            # 对所有结果使用相关性计算规则
+            scored_results = []
+            for item in all_results:
+                # 计算相关性得分
+                relevance_score = calculate_keyword_relevance(keyword, item)
+                
+                # 降低相关性阈值，确保更多结果被保留 (从0.5改为0.1)
+                if relevance_score > 0.1:
+                    item['relevance_score'] = relevance_score
+                    scored_results.append(item)
+                    logger.info(f"匹配项: {item['title']}, 相关性得分: {relevance_score}")
+            
+            # 按相关性得分排序
+            scored_results.sort(key=lambda x: x.get('relevance_score', 0), reverse=True)
+            
+            # 确保精确匹配的结果置顶
+            if exact_matches:
+                for exact_match in exact_matches:
+                    exact_match['relevance_score'] = 20.0  # 超高分确保置顶
+                
+                # 移除scored_results中已经存在于exact_matches的项
+                exact_ids = {item['id'] for item in exact_matches}
+                scored_results = [item for item in scored_results if item['id'] not in exact_ids]
+                
+                # 合并两个结果集
+                results = exact_matches + scored_results
+            else:
+                results = scored_results
+            
+            # 限制返回结果数量
+            results = results[:50]
+            
+    except Exception as e:
+        logger.error(f"搜索出错: {e}")
+        import traceback
+        traceback.print_exc()
+        return jsonify({"error": "搜索系统异常，请稍后再试"}), 500
+    finally:
+        conn.close()
+    
+    # 第二阶段：根据指定方式排序
+    if results:
+        if sort_by == "similarity" or not sort_by:
+            # 保持按相关性得分排序，已经排好了
+            pass
+        elif sort_by == "downloads":
+            results.sort(key=lambda x: x.get("download_count", 0), reverse=True)
+        elif sort_by == "downloads_asc":
+            results.sort(key=lambda x: x.get("download_count", 0))
+        elif sort_by == "newest":
+            results.sort(key=lambda x: x.get("create_time", ""), reverse=True)
+        elif sort_by == "oldest":
+            results.sort(key=lambda x: x.get("create_time", ""))
+        elif sort_by == "title_asc":
+            results.sort(key=lambda x: x.get("title", ""))
+        elif sort_by == "title_desc":
+            results.sort(key=lambda x: x.get("title", ""), reverse=True)
+    
+    # 最终处理：清理不需要返回的字段，并将 datetime 转为字符串
+    for item in results:
+        item.pop("description", None)
+        item.pop("tags", None)
+        item.pop("relevance_score", None)
+        for k, v in item.items():
+            if hasattr(v, 'isoformat'):
+                item[k] = v.isoformat(sep=' ', timespec='seconds')
+
+    return Response(json.dumps({"results": results}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 推荐功能的API
+@app.route('/recommend_tags', methods=['POST'])
+def recommend_tags():
+    """
+    推荐功能API
+    请求格式：{
+        "user_id": "user1",
+        "tags": ["标签1", "标签2"]  # 可为空
+    }
+    """
+    if request.content_type != 'application/json':
+        return jsonify({"error": "Content-Type must be application/json"}), 415
+
+    data = request.get_json()
+    user_id = data.get("user_id")
+    tags = set(data.get("tags", []))
+
+    # 查询用户已保存的兴趣标签
+    user_tags = set()
+    if user_id:
+        conn = get_db_conn()
+        try:
+            with conn.cursor() as cursor:
+                cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+                user_tags = set(row[0] for row in cursor.fetchall())
+        finally:
+            conn.close()
+
+    # 合并前端传递的tags和用户兴趣标签
+    all_tags = list(tags | user_tags)
+
+    if not all_tags:
+        return Response(json.dumps({"error": "暂无推荐结果"}, ensure_ascii=False), mimetype='application/json; charset=utf-8'), 200
+
+    conn = get_db_conn()
+    try:
+        with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+            # 优先用tags字段匹配
+            # 先查找所有tag_id
+            tag_ids = []
+            for tag in all_tags:
+                cursor.execute("SELECT id FROM tags WHERE name=%s", (tag,))
+                row = cursor.fetchone()
+                if row:
+                    tag_ids.append(row['id'])
+            if not tag_ids:
+                return Response(json.dumps({"error": "暂无推荐结果"}, ensure_ascii=False), mimetype='application/json; charset=utf-8'), 200
+            tag_placeholders = ','.join(['%s'] * len(tag_ids))
+            sql = f"""
+                SELECT p.id, p.title, tp.name as category, p.heat,
+                       GROUP_CONCAT(tg.name) as tags
+                FROM posts p
+                LEFT JOIN post_tags pt ON p.id = pt.post_id
+                LEFT JOIN tags tg ON pt.tag_id = tg.id
+                LEFT JOIN topics tp ON p.topic_id = tp.id
+                WHERE pt.tag_id IN ({tag_placeholders})
+                GROUP BY p.id
+                LIMIT 50
+            """
+            cursor.execute(sql, tuple(tag_ids))
+            results = cursor.fetchall()
+            # 若无结果，回退title/content模糊匹配
+            if not results:
+                or_conditions = []
+                params = []
+                for tag in all_tags:
+                    or_conditions.append("p.title LIKE %s OR p.content LIKE %s")
+                    params.extend(['%' + tag + '%', '%' + tag + '%'])
+                where_clause = ' OR '.join(or_conditions)
+                sql = f"""
+                    SELECT p.id, p.title, tp.name as category, p.heat,
+                           GROUP_CONCAT(tg.name) as tags
+                    FROM posts p
+                    LEFT JOIN post_tags pt ON p.id = pt.post_id
+                    LEFT JOIN tags tg ON pt.tag_id = tg.id
+                    LEFT JOIN topics tp ON p.topic_id = tp.id
+                    WHERE {where_clause}
+                    GROUP BY p.id
+                    LIMIT 50
+                """
+                cursor.execute(sql, tuple(params))
+                results = cursor.fetchall()
+    finally:
+        conn.close()
+
+    if not results:
+        return Response(json.dumps({"error": "暂无推荐结果"}, ensure_ascii=False), mimetype='application/json; charset=utf-8'), 200
+
+    return Response(json.dumps({"recommendations": results}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 用户兴趣标签管理API（可选）
+@app.route('/tags', methods=['POST', 'GET', 'DELETE'])
+def user_tags():
+    """
+    POST: 添加用户兴趣标签
+    GET: 查询用户兴趣标签
+    DELETE: 删除用户兴趣标签
+    """
+    if request.method == 'POST':
+        if request.content_type != 'application/json':
+            return jsonify({"error": "Content-Type must be application/json"}), 415
+        data = request.get_json()
+        user_id = data.get("user_id")
+        tags = data.get("tags", [])
+        
+        if not user_id:
+            return jsonify({"error": "用户ID不能为空"}), 400
+        
+        # 确保标签列表格式正确
+        if isinstance(tags, str):
+            tags = [tag.strip() for tag in tags.split(',') if tag.strip()]
+        
+        if not tags:
+            return jsonify({"error": "标签不能为空"}), 400
+        
+        conn = get_db_conn()
+        try:
+            with conn.cursor() as cursor:
+                # 添加用户标签
+                for tag in tags:
+                    # 先查找tag_id
+                    cursor.execute("SELECT id FROM tags WHERE name=%s", (tag,))
+                    tag_row = cursor.fetchone()
+                    if tag_row:
+                        tag_id = tag_row[0]
+                        cursor.execute("REPLACE INTO user_tags (user_id, tag_id) VALUES (%s, %s)", (user_id, tag_id))
+                conn.commit()
+                # 返回更新后的标签列表
+                cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+                updated_tags = [row[0] for row in cursor.fetchall()]
+        finally:
+            conn.close()
+        return Response(json.dumps({"msg": "添加成功", "tags": updated_tags}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    elif request.method == 'DELETE':
+        if request.content_type != 'application/json':
+            return jsonify({"error": "Content-Type must be application/json"}), 415
+        data = request.get_json()
+        user_id = data.get("user_id")
+        tags = data.get("tags", [])
+        if not user_id:
+            return jsonify({"error": "用户ID不能为空"}), 400
+        if not tags:
+            return jsonify({"error": "标签不能为空"}), 400
+        
+        conn = get_db_conn()
+        try:
+            with conn.cursor() as cursor:
+                for tag in tags:
+                    cursor.execute("SELECT id FROM tags WHERE name=%s", (tag,))
+                    tag_row = cursor.fetchone()
+                    if tag_row:
+                        tag_id = tag_row[0]
+                        cursor.execute("DELETE FROM user_tags WHERE user_id=%s AND tag_id=%s", (user_id, tag_id))
+                conn.commit()
+                cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+                remaining_tags = [row[0] for row in cursor.fetchall()]
+        finally:
+            conn.close()
+        return Response(json.dumps({"msg": "删除成功", "tags": remaining_tags}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    else:  # GET 请求
+        user_id = request.args.get("user_id")
+        if not user_id:
+            return jsonify({"error": "用户ID不能为空"}), 400
+        conn = get_db_conn()
+        try:
+            with conn.cursor() as cursor:
+                cursor.execute("SELECT t.name FROM user_tags ut JOIN tags t ON ut.tag_id = t.id WHERE ut.user_id=%s", (user_id,))
+                tags = [row[0] for row in cursor.fetchall()]
+        finally:
+            conn.close()
+        return Response(json.dumps({"tags": tags}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 添加/user_tags路由作为/tags的别名
+@app.route('/user_tags', methods=['POST', 'GET', 'DELETE'])
+def user_tags_alias():
+    """
+    /user_tags路由 - 作为/tags路由的别名
+    POST: 添加用户兴趣标签
+    GET: 查询用户兴趣标签
+    DELETE: 删除用户兴趣标签
+    """
+    return user_tags()
+
+# 基于用户的协同过滤推荐API
+@app.route('/user_based_recommend', methods=['POST'])
+def user_based_recommend():
+    """
+    基于用户的协同过滤推荐API
+    请求格式：{
+        "user_id": "user1",
+        "top_n": 5
+    }
+    """
+    if request.content_type != 'application/json':
+        return jsonify({"error": "Content-Type must be application/json"}), 415
+
+    data = request.get_json()
+    user_id = data.get("user_id")
+    top_n = int(data.get("top_n", 5))
+
+    if not user_id:
+        return jsonify({"error": "用户ID不能为空"}), 400
+    
+    conn = get_db_conn()
+    try:
+        with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+            # 1. 检查用户是否存在下载记录（收藏或浏览）
+            cursor.execute("""
+                SELECT COUNT(*) as count
+                FROM behaviors
+                WHERE user_id = %s AND type IN ('favorite', 'view')
+            """, (user_id,))
+            result = cursor.fetchone()
+            user_download_count = result['count'] if result else 0
+            
+            logger.info(f"用户 {user_id} 下载记录数: {user_download_count}")
+            
+            # 如果用户没有足够的行为数据，返回基于热度的推荐
+            if user_download_count < 3:
+                logger.info(f"用户 {user_id} 下载记录不足，返回热门推荐")
+                cursor.execute("""
+                    SELECT p.id, p.title, tp.name as category, p.heat
+                    FROM posts p
+                    LEFT JOIN topics tp ON p.topic_id = tp.id
+                    ORDER BY p.heat DESC
+                    LIMIT %s
+                """, (top_n,))
+                popular_seeds = cursor.fetchall()
+                return Response(json.dumps({"recommendations": popular_seeds, "type": "popular"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+            
+            # 2. 获取用户已下载（收藏/浏览）的帖子
+            cursor.execute("""
+                SELECT post_id
+                FROM behaviors
+                WHERE user_id = %s AND type IN ('favorite', 'view')
+            """, (user_id,))
+            user_seeds = set(row['post_id'] for row in cursor.fetchall())
+            logger.info(f"用户 {user_id} 已下载种子: {user_seeds}")
+            
+            # 3. 获取所有用户-帖子下载（收藏/浏览）矩阵
+            cursor.execute("""
+                SELECT user_id, post_id
+                FROM behaviors
+                WHERE created_at > DATE_SUB(NOW(), INTERVAL 3 MONTH)
+                AND user_id <> %s AND type IN ('favorite', 'view')
+            """, (user_id,))
+            download_records = cursor.fetchall()
+            
+            if not download_records:
+                logger.info(f"没有其他用户的下载记录，返回热门推荐")
+                cursor.execute("""
+                    SELECT p.id, p.title, tp.name as category, p.heat
+                    FROM posts p
+                    LEFT JOIN topics tp ON p.topic_id = tp.id
+                    ORDER BY p.heat DESC
+                    LIMIT %s
+                """, (top_n,))
+                popular_seeds = cursor.fetchall()
+                return Response(json.dumps({"recommendations": popular_seeds, "type": "popular"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+            
+            # 构建用户-物品矩阵
+            user_item_matrix = {}
+            for record in download_records:
+                uid = record['user_id']
+                sid = record['post_id']
+                if uid not in user_item_matrix:
+                    user_item_matrix[uid] = set()
+                user_item_matrix[uid].add(sid)
+            
+            # 4. 计算用户相似度
+            similar_users = []
+            for other_id, other_seeds in user_item_matrix.items():
+                if other_id == user_id:
+                    continue
+                intersection = len(user_seeds.intersection(other_seeds))
+                union = len(user_seeds.union(other_seeds))
+                if union > 0 and intersection > 0:
+                    similarity = intersection / union
+                    similar_users.append((other_id, similarity, other_seeds))
+            logger.info(f"找到 {len(similar_users)} 个相似用户")
+            similar_users.sort(key=lambda x: x[1], reverse=True)
+            similar_users = similar_users[:5]
+            # 5. 基于相似用户推荐帖子
+            candidate_seeds = {}
+            for similar_user, similarity, seeds in similar_users:
+                logger.info(f"相似用户 {similar_user}, 相似度 {similarity}")
+                for post_id in seeds:
+                    if post_id not in user_seeds:
+                        if post_id not in candidate_seeds:
+                            candidate_seeds[post_id] = 0
+                        candidate_seeds[post_id] += similarity
+            if not candidate_seeds:
+                logger.info(f"没有找到候选种子，返回热门推荐")
+                cursor.execute("""
+                    SELECT p.id, p.title, tp.name as category, p.heat
+                    FROM posts p
+                    LEFT JOIN topics tp ON p.topic_id = tp.id
+                    ORDER BY p.heat DESC
+                    LIMIT %s
+                """, (top_n,))
+                popular_seeds = cursor.fetchall()
+                return Response(json.dumps({"recommendations": popular_seeds, "type": "popular"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+            # 6. 获取推荐帖子的详细信息
+            recommended_seeds = sorted(candidate_seeds.items(), key=lambda x: x[1], reverse=True)[:top_n]
+            post_ids = [post_id for post_id, _ in recommended_seeds]
+            format_strings = ','.join(['%s'] * len(post_ids))
+            cursor.execute(f"""
+                SELECT p.id, p.title, tp.name as category, p.heat
+                FROM posts p
+                LEFT JOIN topics tp ON p.topic_id = tp.id
+                WHERE p.id IN ({format_strings})
+            """, tuple(post_ids))
+            result_seeds = cursor.fetchall()
+            seed_score_map = {post_id: score for post_id, score in recommended_seeds}
+            result_seeds.sort(key=lambda x: seed_score_map.get(x['id'], 0), reverse=True)
+            logger.info(f"返回 {len(result_seeds)} 个基于协同过滤的推荐")
+            return Response(json.dumps({"recommendations": result_seeds, "type": "collaborative"}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    except Exception as e:
+        logger.error(f"推荐系统错误: {e}")
+        import traceback
+        traceback.print_exc()
+        return Response(json.dumps({"error": "推荐系统异常，请稍后再试", "details": str(e)}, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    finally:
+        conn.close()
+@app.route('/word2vec_status', methods=['GET'])
+def word2vec_status():
+    """
+    检查Word2Vec模型状态
+    返回模型是否加载、词汇量等信息
+    """
+    if not WORD2VEC_ENABLED:
+        return Response(json.dumps({
+            "enabled": False,
+            "message": "Word2Vec功能未启用"
+        }, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    try:
+        helper = get_word2vec_helper()
+        status = {
+            "enabled": WORD2VEC_ENABLED,
+            "initialized": helper.initialized,
+            "vocab_size": len(helper.model.index_to_key) if helper.model else 0,
+            "vector_size": helper.model.vector_size if helper.model else 0
+        }
+        
+        # 测试几个常用词的相似词，展示模型效果
+        test_results = {}
+        test_words = ["电影", "动作", "科幻", "动漫", "游戏"]
+        for word in test_words:
+            similar_words = helper.get_similar_words(word, topn=5)
+            test_results[word] = similar_words
+        
+        status["test_results"] = test_results
+        return Response(json.dumps(status, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    except Exception as e:
+        return Response(json.dumps({
+            "enabled": WORD2VEC_ENABLED,
+            "initialized": False,
+            "error": str(e)
+        }, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+
+# 添加一个临时诊断端点
+@app.route('/debug_search', methods=['POST'])
+def debug_search():
+    """临时的调试端点，用于检查数据库中的记录"""
+    if request.content_type != 'application/json':
+        return jsonify({"error": "Content-Type must be application/json"}), 415
+
+    data = request.get_json()
+    keyword = data.get("keyword", "").strip()
+    
+    conn = get_db_conn()
+    try:
+        with conn.cursor(pymysql.cursors.DictCursor) as cursor:
+            # 尝试查询包含特定词的所有记录
+            queries = [
+                ("标题中包含关键词", f"SELECT seed_id, title, description, tags FROM pt_seed WHERE title LIKE '%{keyword}%' LIMIT 10"),
+                ("描述中包含关键词", f"SELECT seed_id, title, description, tags FROM pt_seed WHERE description LIKE '%{keyword}%' LIMIT 10"),
+                ("标签中包含关键词", f"SELECT seed_id, title, description, tags FROM pt_seed WHERE FIND_IN_SET('{keyword}', tags) LIMIT 10"),
+                ("肖申克的救赎", "SELECT seed_id, title, description, tags FROM pt_seed WHERE title = '肖申克的救赎'")
+            ]
+            
+            results = {}
+            for query_name, query in queries:
+                cursor.execute(query)
+                results[query_name] = cursor.fetchall()
+                
+            return Response(json.dumps(results, ensure_ascii=False), mimetype='application/json; charset=utf-8')
+    finally:
+        conn.close()
+
+"""
+接口本地测试方法（可直接运行main_online.py后用curl或Postman测试）：
+
+1. 搜索接口
+curl -X POST http://127.0.0.1:5000/search -H "Content-Type: application/json" -d '{"keyword":"电影","sort_by":"downloads"}'
+
+2. 标签推荐接口
+curl -X POST http://127.0.0.1:5000/recommend_tags -H "Content-Type: application/json" -d '{"user_id":"1","tags":["动作","科幻"]}'
+
+3. 用户兴趣标签管理（添加标签）
+curl -X POST http://127.0.0.1:5000/user_tags -H "Content-Type: application/json" -d '{"user_id":"1","tags":["动作","科幻"]}'
+
+4. 用户兴趣标签管理（查询标签）
+curl "http://127.0.0.1:5000/user_tags?user_id=1"
+
+5. 用户兴趣标签管理（删除标签）
+curl -X DELETE http://127.0.0.1:5000/user_tags -H "Content-Type: application/json" -d '{"user_id":"1","tags":["动作","科幻"]}'
+
+6. 协同过滤推荐
+curl -X POST http://127.0.0.1:5000/user_based_recommend -H "Content-Type: application/json" -d '{"user_id":"user1","top_n":3}'
+
+7. Word2Vec状态检查
+curl "http://127.0.0.1:5000/word2vec_status"
+
+8. 调试接口（临时）
+curl -X POST http://127.0.0.1:5000/debug_search -H "Content-Type: application/json" -d '{"keyword":"电影"}'
+
+所有接口均可用Postman按上述参数测试。
+"""
+
+if __name__ == "__main__":
+    try:
+        logger.info("搜索推荐服务启动中...")
+        app.run(host="0.0.0.0", port=5000)
+    except Exception as e:
+        logger.error(f"启动异常: {e}")
+        import traceback
+        traceback.print_exc()
diff --git a/Merge/back_jwlll/config.py b/Merge/back_jwlll/config.py
new file mode 100644
index 0000000..77cb8dc
--- /dev/null
+++ b/Merge/back_jwlll/config.py
@@ -0,0 +1,38 @@
+# JWLLL 搜索推荐服务配置
+
+# 数据库配置
+DB_CONFIG = {
+    "host": "10.126.59.25",
+    "port": 3306,
+    "user": "root",
+    "password": "123456",
+    "database": "redbook",
+    "charset": "utf8mb4"
+}
+
+# 服务器配置
+SERVER_CONFIG = {
+    "host": "127.0.0.1",
+    "port": 5000,
+    "debug": True
+}
+
+# Word2Vec 模型配置
+WORD2VEC_CONFIG = {
+    "model_path": "models/chinese_word2vec.bin",
+    "enabled": True  # 如果没有模型文件，设置为 False
+}
+
+# 搜索推荐配置
+SEARCH_CONFIG = {
+    "default_user_id": "3",
+    "default_tags": ["美食", "影视", "穿搭"],
+    "max_results": 50,
+    "similarity_threshold": 0.1
+}
+
+# 日志配置
+LOGGING_CONFIG = {
+    "level": "INFO",
+    "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+}
diff --git a/Merge/back_jwlll/requirements.txt b/Merge/back_jwlll/requirements.txt
new file mode 100644
index 0000000..6e7bd22
--- /dev/null
+++ b/Merge/back_jwlll/requirements.txt
@@ -0,0 +1,9 @@
+Flask==2.3.3
+pymysql==1.1.0
+scikit-learn==1.3.2
+jieba==0.42.1
+pypinyin==0.49.0
+flask-cors==4.0.0
+numpy==1.24.4
+Levenshtein==0.23.0
+gensim==4.3.2
diff --git a/Merge/back_jwlll/semantic_config.json b/Merge/back_jwlll/semantic_config.json
new file mode 100644
index 0000000..f5e34d3
--- /dev/null
+++ b/Merge/back_jwlll/semantic_config.json
@@ -0,0 +1,77 @@
+{
+  "国宝": ["熊猫", "大熊猫", "功夫熊猫", "四川", "成都", "保护动物"],
+  "熊猫": ["国宝", "大熊猫", "功夫熊猫", "竹子", "四川", "黑白"],
+  "功夫": ["武术", "格斗", "武打", "功夫熊猫", "李小龙", "成龙", "太极", "截拳道", "中国功夫"],
+  
+  "梦": ["梦想", "梦境", "白日梦", "白日梦想家", "潜意识", "睡眠", "做梦"],
+  "白日梦": ["梦想", "幻想", "白日梦想家", "想象", "憧憬"],
+  
+  "魔戒": ["指环王", "魔戒再现", "中土世界", "霍比特人", "精灵", "魔法", "奇幻"],
+  "指环": ["魔戒", "指环王", "戒指", "魔戒再现", "首饰"],
+  "中土世界": ["魔戒", "指环王", "霍比特人", "精灵", "矮人", "奇幻"],
+  
+  "漫威": ["复仇者", "钢铁侠", "蜘蛛侠", "美国队长", "雷神", "绿巨人", "黑寡妇", "惊奇队长", "超级英雄", "漫画"],
+  "钢铁侠": ["托尼斯塔克", "钢铁战衣", "贾维斯", "复仇者", "漫威", "超级英雄"],
+  "蜘蛛侠": ["彼得帕克", "蜘蛛", "纽约", "漫威", "超级英雄", "蜘蛛感应"],
+  
+  "DC": ["蝙蝠侠", "超人", "神奇女侠", "正义联盟", "闪电侠", "水行侠", "超级英雄", "漫画"],
+  "蝙蝠侠": ["布鲁斯韦恩", "高谭市", "小丑", "罗宾", "DC", "超级英雄"],
+  "超人": ["克拉克肯特", "氪星", "莱克斯卢瑟", "超能力", "DC", "超级英雄"],
+  
+  "星球大战": ["星战", "原力", "天行者", "达斯维达", "尤达", "绝地武士", "光剑", "帝国", "科幻"],
+  "原力": ["绝地武士", "星球大战", "天行者", "尤达", "光剑", "西斯", "科幻"],
+  
+  "哈利波特": ["魔法", "霍格沃茨", "魔杖", "魔法石", "伏地魔", "巫师", "奇幻", "魔幻"],
+  "魔法": ["巫师", "法术", "咒语", "哈利波特", "霍格沃茨", "魔杖", "奇幻", "魔幻"],
+  
+  "科幻": ["未来", "太空", "星际", "外星人", "人工智能", "机器人", "时空", "星球大战", "星际穿越"],
+  "太空": ["宇宙", "星球", "卫星", "宇航员", "航天", "科幻", "星际", "外太空"],
+  "人工智能": ["AI", "机器学习", "深度学习", "神经网络", "机器人", "算法", "科技", "科幻"],
+  
+  "动作": ["武打", "格斗", "功夫", "特技", "追逐", "冒险", "刺激", "爆破"],
+  "冒险": ["探险", "奇遇", "探索", "未知", "旅程", "冒险家", "刺激", "危险"],
+  "奇幻": ["魔法", "魔幻", "神话", "异世界", "精灵", "龙", "魔戒", "哈利波特"],
+  
+  "悬疑": ["推理", "谜题", "侦探", "神秘", "悬念", "惊悚", "犯罪", "悬疑片"],
+  "推理": ["侦探", "线索", "谜题", "破案", "悬疑", "逻辑", "智力", "悬疑片"],
+  
+  "恐怖": ["惊悚", "鬼怪", "恶魔", "惊吓", "血腥", "恐怖片", "心理恐惧", "超自然"],
+  "鬼怪": ["幽灵", "鬼魂", "妖怪", "超自然", "恐怖", "惊悚", "诡异", "恐怖片"],
+  
+  "喜剧": ["搞笑", "幽默", "欢乐", "笑声", "喜剧片", "滑稽", "逗乐", "喜剧演员"],
+  "搞笑": ["幽默", "笑话", "喜剧", "逗乐", "滑稽", "欢乐", "喜剧片", "喜剧演员"],
+  
+  "战争": ["军事", "战场", "士兵", "军队", "战役", "武器", "战争片", "历史战争"],
+  "军事": ["军队", "武器", "战争", "军人", "战略", "战术", "国防", "军事片"],
+  
+  "剧情": ["情节", "故事", "叙事", "人物", "感人", "真实", "戏剧性", "剧情片"],
+  "历史": ["古代", "历史事件", "历史人物", "朝代", "文明", "历史片", "传记", "纪实"],
+  
+  "纪录片": ["真实记录", "纪实", "历史", "自然", "科学", "社会", "文化", "探索"],
+  "动画": ["卡通", "动漫", "动画片", "动画电影", "CG", "3D动画", "手绘", "二次元"],
+  
+  "音乐": ["歌曲", "旋律", "节奏", "乐器", "演唱", "音乐家", "音乐剧", "音乐会"],
+  "歌曲": ["歌词", "唱歌", "歌手", "流行歌曲", "音乐", "专辑", "单曲", "MV"],
+  
+  "爱情": ["恋爱", "浪漫", "情侣", "爱情故事", "爱情片", "感情", "爱意", "约会"],
+  "浪漫": ["爱情", "情感", "温馨", "甜蜜", "爱意", "爱情片", "情侣", "表白"],
+  
+  "Netflix": ["网飞", "流媒体", "自制剧", "电视剧", "纸牌屋", "怪奇物语", "王冠", "订阅"],
+  "迪士尼": ["米老鼠", "唐老鸭", "公主", "动画", "迪士尼乐园", "皮克斯", "童话", "漫威"],
+  
+  "游戏": ["电子游戏", "游戏机", "主机游戏", "PC游戏", "手游", "网游", "单机", "多人游戏"],
+  "动漫": ["日本动画", "漫画", "二次元", "动画", "动画片", "ACGN", "宅文化", "御宅族"],
+  
+  "日本": ["东京", "京都", "大阪", "日本文化", "日本料理", "樱花", "动漫", "武士道"],
+  "美国": ["纽约", "洛杉矶", "华盛顿", "美国文化", "好莱坞", "自由女神像", "美式"],
+  
+  "教育": ["学习", "知识", "课程", "教学", "学校", "教科书", "老师", "学生"],
+  "技术": ["科技", "工程", "编程", "软件", "硬件", "开发", "技术革新", "IT"],
+  
+  "监狱": ["越狱", "囚犯", "牢房", "服刑", "狱警"],
+  "越狱": ["监狱", "囚犯", "逃狱", "越狱计划", "监狱逃脱"],
+  
+  "肥皂": ["手工皂", "皂"],
+  "手工皂": ["肥皂", "皂"],
+  "皂": ["肥皂", "手工皂"]
+}
diff --git a/Merge/back_jwlll/word2vec_helper.py b/Merge/back_jwlll/word2vec_helper.py
new file mode 100644
index 0000000..ecd1a72
--- /dev/null
+++ b/Merge/back_jwlll/word2vec_helper.py
@@ -0,0 +1,279 @@
+# word2vec_helper.py
+# Word2Vec模型加载与使用的辅助模块
+
+import os
+import numpy as np
+from gensim.models import KeyedVectors, Word2Vec
+import jieba
+import logging
+import time
+
+# 设置日志
+logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
+
+class Word2VecHelper:
+    def __init__(self, model_path=None):
+        """
+        初始化Word2Vec辅助类
+        
+        参数:
+            model_path: 预训练模型路径，支持word2vec格式和二进制格式
+                        如果为None，将使用默认路径或尝试下载小型模型
+        """
+        self.model = None
+        
+        # 更改默认模型路径和备用选项
+        if model_path:
+            self.model_path = model_path
+        else:
+            # 首选路径 - 大型腾讯模型
+            primary_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 
+                                      "models", "chinese_word2vec.bin")
+            
+            # 备用路径 - 小型模型
+            backup_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 
+                                     "models", "chinese_word2vec_small.bin")
+            
+            if os.path.exists(primary_path):
+                self.model_path = primary_path
+            elif os.path.exists(backup_path):
+                self.model_path = backup_path
+            else:
+                # 如果都不存在，可以尝试自动下载小模型
+                self.model_path = primary_path
+                self._try_download_small_model()
+        
+        self.initialized = False
+        # 缓存查询结果，提高性能
+        self.similarity_cache = {}
+        self.similar_words_cache = {}
+    
+    def _try_download_small_model(self):
+        """尝试下载小型词向量模型作为备用选项"""
+        try:
+            import gensim.downloader as api
+            logging.info("尝试下载小型中文词向量模型...")
+            
+            # 创建模型目录
+            os.makedirs(os.path.dirname(self.model_path), exist_ok=True)
+            
+            # 尝试下载fastText的小型中文模型
+            small_model = api.load("fasttext-wiki-news-subwords-300")
+            small_model.save(self.model_path.replace(".bin", "_small.bin"))
+            logging.info(f"小型模型已下载并保存到 {self.model_path}")
+        except Exception as e:
+            logging.error(f"无法下载备用模型: {e}")
+
+    def load_model(self):
+        """加载Word2Vec模型"""
+        try:
+            start_time = time.time()
+            logging.info(f"开始加载Word2Vec模型: {self.model_path}")
+            
+            # 判断文件扩展名，选择合适的加载方式
+            if self.model_path.endswith('.bin'):
+                # 加载二进制格式的模型
+                self.model = KeyedVectors.load_word2vec_format(self.model_path, binary=True)
+            else:
+                # 加载文本格式的模型或gensim模型
+                self.model = Word2Vec.load(self.model_path).wv
+                
+            self.initialized = True
+            logging.info(f"Word2Vec模型加载完成，耗时 {time.time() - start_time:.2f} 秒")
+            logging.info(f"词向量维度: {self.model.vector_size}")
+            logging.info(f"词汇表大小: {len(self.model.index_to_key)}")
+            return True
+        except Exception as e:
+            logging.error(f"加载Word2Vec模型失败: {e}")
+            self.initialized = False
+            return False
+    
+    def ensure_initialized(self):
+        """确保模型已初始化"""
+        if not self.initialized:
+            return self.load_model()
+        return True
+    
+    def get_similar_words(self, word, topn=10, min_similarity=0.5):
+        """
+        获取与给定词语最相似的词语列表
+        
+        参数:
+            word: 输入词语
+            topn: 返回相似词的数量
+            min_similarity: 最小相似度阈值
+        返回:
+            相似词列表，如果词不存在或模型未加载则返回空列表
+        """
+        if not self.ensure_initialized():
+            return []
+            
+        # 检查缓存
+        cache_key = f"{word}_{topn}_{min_similarity}"
+        if cache_key in self.similar_words_cache:
+            return self.similar_words_cache[cache_key]
+        
+        try:
+            # 如果词不在词汇表中，进行分词处理
+            if word not in self.model.key_to_index:
+                # 对中文词进行分词，然后查找每个子词的相似词
+                word_parts = list(jieba.cut(word))
+                
+                if not word_parts:
+                    return []
+                
+                # 如果存在多个子词，找到存在于模型中的子词
+                valid_parts = [w for w in word_parts if w in self.model.key_to_index]
+                
+                if not valid_parts:
+                    return []
+                
+                # 使用最长的有效子词或第一个有效子词
+                valid_parts.sort(key=len, reverse=True)
+                word = valid_parts[0]
+                
+                # 如果替换后的词仍不在词汇表中，返回空列表
+                if word not in self.model.key_to_index:
+                    return []
+            
+            # 获取相似词
+            similar_words = self.model.most_similar(word, topn=topn*2)  # 多获取一些，后续过滤
+            
+            # 过滤低于阈值的结果，并只返回词语(不返回相似度)
+            filtered_words = [w for w, sim in similar_words if sim >= min_similarity][:topn]
+            
+            # 缓存结果
+            self.similar_words_cache[cache_key] = filtered_words
+            return filtered_words
+            
+        except Exception as e:
+            logging.error(f"获取相似词失败: {e}, 词语: {word}")
+            return []
+    
+    def calculate_similarity(self, word1, word2):
+        """
+        计算两个词的相似度
+        
+        参数:
+            word1, word2: 输入词语
+        返回:
+            相似度分数(0-1)，如果任意词不存在则返回0
+        """
+        if not self.ensure_initialized():
+            return 0
+            
+        # 检查缓存
+        cache_key = f"{word1}_{word2}"
+        reverse_key = f"{word2}_{word1}"
+        
+        if cache_key in self.similarity_cache:
+            return self.similarity_cache[cache_key]
+        if reverse_key in self.similarity_cache:
+            return self.similarity_cache[reverse_key]
+        
+        try:
+            # 检查词是否在词汇表中
+            if word1 not in self.model.key_to_index or word2 not in self.model.key_to_index:
+                return 0
+            
+            similarity = self.model.similarity(word1, word2)
+            
+            # 缓存结果
+            self.similarity_cache[cache_key] = similarity
+            return similarity
+            
+        except Exception as e:
+            logging.error(f"计算相似度失败: {e}, 词语: {word1}, {word2}")
+            return 0
+    
+    def expand_query(self, query, topn=5, min_similarity=0.6):
+        """
+        扩展查询词，返回相关词汇
+        
+        参数:
+            query: 查询词
+            topn: 每个词扩展的相似词数量
+            min_similarity: 最小相似度阈值
+        返回:
+            扩展后的词语列表
+        """
+        if not self.ensure_initialized():
+            return [query]
+            
+        expanded_terms = [query]
+        
+        # 对查询进行分词
+        words = list(jieba.cut(query))
+        
+        # 为每个词找相似词
+        for word in words:
+            if len(word) <= 1:  # 忽略单字，减少噪音
+                continue
+                
+            similar_words = self.get_similar_words(word, topn=topn, min_similarity=min_similarity)
+            expanded_terms.extend(similar_words)
+        
+        # 确保唯一性
+        return list(set(expanded_terms))
+
+# 单例模式，全局使用一个模型实例
+_word2vec_helper = None
+
+def get_word2vec_helper(model_path=None):
+    """获取Word2Vec辅助类的全局单例"""
+    global _word2vec_helper
+    if _word2vec_helper is None:
+        _word2vec_helper = Word2VecHelper(model_path)
+        _word2vec_helper.ensure_initialized()
+    return _word2vec_helper
+
+# 便捷函数，方便直接调用
+def get_similar_words(word, topn=10, min_similarity=0.5):
+    """获取相似词的便捷函数"""
+    helper = get_word2vec_helper()
+    return helper.get_similar_words(word, topn, min_similarity)
+
+def calculate_similarity(word1, word2):
+    """计算相似度的便捷函数"""
+    helper = get_word2vec_helper()
+    return helper.calculate_similarity(word1, word2)
+
+def expand_query(query, topn=5, min_similarity=0.6):
+    """扩展查询的便捷函数"""
+    helper = get_word2vec_helper()
+    return helper.expand_query(query, topn, min_similarity)
+
+# 使用示例
+if __name__ == "__main__":
+    # 测试模型加载和词语相似度
+    helper = get_word2vec_helper()
+    
+    # 测试词
+    test_words = ["电影", "功夫", "熊猫", "科幻", "漫威"]
+    
+    for word in test_words:
+        print(f"\n{word} 的相似词:")
+        similar = helper.get_similar_words(word, topn=5)
+        for sim_word in similar:
+            print(f"  - {sim_word}")
+    
+    # 测试相似度计算
+    word_pairs = [
+        ("电影", "电视"),
+        ("功夫", "武术"),
+        ("科幻", "未来"),
+        ("漫威", "超级英雄")
+    ]
+    
+    print("\n词语相似度:")
+    for w1, w2 in word_pairs:
+        sim = helper.calculate_similarity(w1, w2)
+        print(f"  {w1} <-> {w2}: {sim:.4f}")
+    
+    # 测试查询扩展
+    test_queries = ["功夫熊猫", "科幻电影", "漫威英雄"]
+    
+    print("\n查询扩展:")
+    for query in test_queries:
+        expanded = helper.expand_query(query)
+        print(f"  {query} -> {expanded}")
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diff --git a/Merge/back_rhj/requirements.txt b/Merge/back_rhj/requirements.txt
new file mode 100644
index 0000000..9886994
--- /dev/null
+++ b/Merge/back_rhj/requirements.txt
@@ -0,0 +1,10 @@
+Flask==2.3.3
+Flask-CORS==4.0.0
+python-dotenv==1.0.0
+SQLAlchemy==2.0.23
+PyMySQL==1.1.0
+torch==2.1.0
+numpy==1.24.3
+PyJWT==2.8.0
+Flask-Mail==0.9.1
+APScheduler==3.10.4
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diff --git a/Merge/front/src/api/search_jwlll.js b/Merge/front/src/api/search_jwlll.js
new file mode 100644
index 0000000..5ab7eb1
--- /dev/null
+++ b/Merge/front/src/api/search_jwlll.js
@@ -0,0 +1,97 @@
+// 搜索推荐算法相关的API接口
+// 对应 JWLLL 后端服务
+
+const BASE_URL = 'http://127.0.0.1:5000'
+
+// 通用请求函数
+const request = async (url, options = {}) => {
+  try {
+    const response = await fetch(url, {
+      headers: {
+        'Content-Type': 'application/json',
+        ...options.headers
+      },
+      ...options
+    })
+    return await response.json()
+  } catch (error) {
+    console.error('API请求错误:', error)
+    throw error
+  }
+}
+
+// 搜索API
+export const searchAPI = {
+  // 搜索内容
+  search: async (keyword, category = undefined) => {
+    return await request(`${BASE_URL}/search`, {
+      method: 'POST',
+      body: JSON.stringify({ keyword, category })
+    })
+  },
+
+  // 获取用户标签
+  getUserTags: async (userId) => {
+    return await request(`${BASE_URL}/user_tags?user_id=${userId}`)
+  },
+
+  // 标签推荐
+  recommendByTags: async (userId, tags) => {
+    return await request(`${BASE_URL}/recommend_tags`, {
+      method: 'POST',
+      body: JSON.stringify({ user_id: userId, tags })
+    })
+  },
+
+  // 协同过滤推荐
+  userBasedRecommend: async (userId, topN = 20) => {
+    return await request(`${BASE_URL}/user_based_recommend`, {
+      method: 'POST',
+      body: JSON.stringify({ user_id: userId, top_n: topN })
+    })
+  },
+
+  // 获取帖子详情
+  getPostDetail: async (postId) => {
+    return await request(`${BASE_URL}/post/${postId}`)
+  },
+
+  // 点赞帖子
+  likePost: async (postId, userId) => {
+    return await request(`${BASE_URL}/like`, {
+      method: 'POST',
+      body: JSON.stringify({ post_id: postId, user_id: userId })
+    })
+  },
+
+  // 取消点赞
+  unlikePost: async (postId, userId) => {
+    return await request(`${BASE_URL}/unlike`, {
+      method: 'POST',
+      body: JSON.stringify({ post_id: postId, user_id: userId })
+    })
+  },
+
+  // 添加评论
+  addComment: async (postId, userId, content) => {
+    return await request(`${BASE_URL}/comment`, {
+      method: 'POST',
+      body: JSON.stringify({ post_id: postId, user_id: userId, content })
+    })
+  },
+
+  // 获取评论
+  getComments: async (postId) => {
+    return await request(`${BASE_URL}/comments/${postId}`)
+  },
+
+  // 上传帖子
+  uploadPost: async (postData) => {
+    return await request(`${BASE_URL}/upload`, {
+      method: 'POST',
+      body: JSON.stringify(postData)
+    })
+  }
+}
+
+export default searchAPI
diff --git a/Merge/front/src/components/CreatePost.jsx b/Merge/front/src/components/CreatePost.jsx
index 1d2f306..c11e247 100644
--- a/Merge/front/src/components/CreatePost.jsx
+++ b/Merge/front/src/components/CreatePost.jsx
@@ -13,10 +13,8 @@
   const navigate = useNavigate()
   const { postId } = useParams()
   const isEdit = Boolean(postId)
-
   // 步骤：新帖先上传，编辑则直接到 detail
   const [step, setStep] = useState(isEdit ? 'detail' : 'upload')
-  const [files, setFiles] = useState([])
   const [mediaUrls, setMediaUrls] = useState([])
 
   // 表单字段
@@ -53,10 +51,8 @@
       .catch(err => setError(err.message))
       .finally(() => setLoading(false))
   }, [isEdit, postId])
-
   // 上传回调
   const handleUploadComplete = async uploadedFiles => {
-    setFiles(uploadedFiles)
     // TODO: 真正上传到服务器后替换为服务端 URL
     const urls = await Promise.all(
       uploadedFiles.map(f => URL.createObjectURL(f))
diff --git a/Merge/front/src/components/HomeFeed.jsx b/Merge/front/src/components/HomeFeed.jsx
index c681858..e32a2eb 100644
--- a/Merge/front/src/components/HomeFeed.jsx
+++ b/Merge/front/src/components/HomeFeed.jsx
@@ -1,9 +1,10 @@
 // src/components/HomeFeed.jsx
 
-import React, { useState, useEffect } from 'react'
+import React, { useState, useEffect, useCallback } from 'react'
 import { useNavigate } from 'react-router-dom'
 import { ThumbsUp } from 'lucide-react'
 import { fetchPosts, fetchPost } from '../api/posts_wzy'
+import { searchAPI } from '../api/search_jwlll'
 import '../style/HomeFeed.css'
 
 const categories = [
@@ -11,15 +12,120 @@
   '职场','情感','家居','游戏','旅行','健身'
 ]
 
+const recommendModes = [
+  { label: '标签推荐', value: 'tag' },
+  { label: '协同过滤推荐', value: 'cf' }
+]
+
+const DEFAULT_USER_ID = '3' // 确保数据库有此用户
+const DEFAULT_TAGS = ['美食','影视','穿搭'] // 可根据实际数据库调整
+
 export default function HomeFeed() {
   const navigate = useNavigate()
   const [activeCat, setActiveCat] = useState('推荐')
   const [items, setItems]         = useState([])
   const [loading, setLoading]     = useState(true)
   const [error, setError]         = useState(null)
+    // JWLLL 搜索推荐相关状态
+  const [search, setSearch] = useState('')
+  const [recMode, setRecMode] = useState('tag')
+  const [recCFNum, setRecCFNum] = useState(20)
+  const [useSearchRecommend, setUseSearchRecommend] = useState(false) // 是否使用搜索推荐模式  // JWLLL 搜索推荐功能函数
+  
+  // JWLLL搜索推荐内容
+  const fetchSearchContent = useCallback(async (keyword = '') => {
+    setLoading(true)
+    setError(null)
+    try {
+      const data = await searchAPI.search(keyword || activeCat, activeCat === '推荐' ? undefined : activeCat)
+      const formattedItems = (data.results || []).map(item => ({
+        id: item.id,
+        title: item.title,
+        author: item.author || '佚名',
+        avatar: `https://i.pravatar.cc/40?img=${item.id}`,
+        img: item.img || '', 
+        likes: item.heat || 0,
+        content: item.content
+      }))
+      setItems(formattedItems)
+    } catch (e) {
+      console.error('搜索失败:', e)
+      setError('搜索失败')
+      setItems([])
+    }
+    setLoading(false)
+  }, [activeCat])
+
+  // 标签推荐
+  const fetchTagRecommend = useCallback(async (tags) => {
+    setLoading(true)
+    setError(null)
+    try {
+      const data = await searchAPI.recommendByTags(DEFAULT_USER_ID, tags)
+      const formattedItems = (data.recommendations || []).map(item => ({
+        id: item.id,
+        title: item.title,
+        author: item.author || '佚名',
+        avatar: `https://i.pravatar.cc/40?img=${item.id}`,
+        img: item.img || '', 
+        likes: item.heat || 0,
+        content: item.content
+      }))
+      setItems(formattedItems)
+    } catch (e) {
+      console.error('标签推荐失败:', e)
+      setError('标签推荐失败')
+      setItems([])
+    }
+    setLoading(false)
+  }, [])
+
+  // 协同过滤推荐
+  const fetchCFRecommend = useCallback(async (topN = recCFNum) => {
+    setLoading(true)
+    setError(null)
+    try {
+      const data = await searchAPI.userBasedRecommend(DEFAULT_USER_ID, topN)
+      const formattedItems = (data.recommendations || []).map(item => ({
+        id: item.id,
+        title: item.title,
+        author: item.author || '佚名',
+        avatar: `https://i.pravatar.cc/40?img=${item.id}`,
+        img: item.img || '', 
+        likes: item.heat || 0,
+        content: item.content
+      }))
+      setItems(formattedItems)
+    } catch (e) {
+      console.error('协同过滤推荐失败:', e)
+      setError('协同过滤推荐失败')
+      setItems([])
+    }
+    setLoading(false)
+  }, [recCFNum])
+
+  // 获取用户兴趣标签后再推荐
+  const fetchUserTagsAndRecommend = useCallback(async () => {
+    setLoading(true)
+    setError(null)
+    let tags = []
+    try {
+      const data = await searchAPI.getUserTags(DEFAULT_USER_ID)
+      tags = Array.isArray(data.tags) && data.tags.length > 0 ? data.tags : DEFAULT_TAGS
+    } catch {
+      tags = DEFAULT_TAGS
+    }
+    if (recMode === 'tag') {
+      await fetchTagRecommend(tags)
+    } else {
+      await fetchCFRecommend()
+    }
+    setLoading(false)
+  }, [recMode, fetchTagRecommend, fetchCFRecommend])
 
   useEffect(() => {
-    async function loadPosts() {
+    // 原始数据加载函数
+    const loadPosts = async () => {
       try {
         const list = await fetchPosts()  // [{id, title, heat, created_at}, …]
         // 为了拿到 media_urls 和 user_id，这里再拉详情
@@ -43,25 +149,149 @@
         setLoading(false)
       }
     }
-    loadPosts()
-  }, [])
 
+    // 根据模式选择加载方式
+    if (activeCat === '推荐' && useSearchRecommend) {
+      fetchUserTagsAndRecommend()
+    } else {
+      loadPosts()
+    }
+  }, [activeCat, useSearchRecommend, fetchUserTagsAndRecommend])
+  // 切换推荐模式时的额外处理
+  useEffect(() => {
+    if (activeCat === '推荐' && useSearchRecommend) {
+      fetchUserTagsAndRecommend()
+    }
+    // eslint-disable-next-line
+  }, [recMode, fetchUserTagsAndRecommend])
+
+  // 根据模式选择不同的加载方式
+  const handleSearch = e => {
+    e.preventDefault()
+    if (useSearchRecommend) {
+      fetchSearchContent(search)
+    } else {
+      // 切换到搜索推荐模式
+      setUseSearchRecommend(true)
+      fetchSearchContent(search)
+    }
+  }
+
+  const handlePostClick = (postId) => {
+    navigate(`/post/${postId}`)
+  }
   return (
     <div className="home-feed">
+      {/* 数据源切换 */}
+      <div style={{marginBottom:12, display:'flex', alignItems:'center', gap:16}}>
+        <span>数据源：</span>
+        <div style={{display:'flex', gap:8}}>
+          <button
+            className={!useSearchRecommend ? 'rec-btn styled active' : 'rec-btn styled'}
+            onClick={() => {setUseSearchRecommend(false); setActiveCat('推荐')}}
+            type="button"
+            style={{
+              borderRadius: 20,
+              padding: '4px 18px',
+              border: !useSearchRecommend ? '2px solid #e84c4a' : '1px solid #ccc',
+              background: !useSearchRecommend ? '#fff0f0' : '#fff',
+              color: !useSearchRecommend ? '#e84c4a' : '#333',
+              fontWeight: !useSearchRecommend ? 600 : 400,
+              cursor: 'pointer',
+              transition: 'all 0.2s',
+              outline: 'none',
+            }}
+          >原始数据</button>
+          <button
+            className={useSearchRecommend ? 'rec-btn styled active' : 'rec-btn styled'}
+            onClick={() => {setUseSearchRecommend(true); setActiveCat('推荐')}}
+            type="button"
+            style={{
+              borderRadius: 20,
+              padding: '4px 18px',
+              border: useSearchRecommend ? '2px solid #e84c4a' : '1px solid #ccc',
+              background: useSearchRecommend ? '#fff0f0' : '#fff',
+              color: useSearchRecommend ? '#e84c4a' : '#333',
+              fontWeight: useSearchRecommend ? 600 : 400,
+              cursor: 'pointer',
+              transition: 'all 0.2s',
+              outline: 'none',
+            }}
+          >智能推荐</button>
+        </div>
+      </div>
+
+      {/* 推荐模式切换，仅在推荐页显示且使用搜索推荐时 */}
+      {activeCat === '推荐' && useSearchRecommend && (
+        <div style={{marginBottom:12, display:'flex', alignItems:'center', gap:16}}>
+          <span style={{marginRight:8}}>推荐模式：</span>
+          <div style={{display:'flex', gap:8}}>
+            {recommendModes.map(m => (
+              <button
+                key={m.value}
+                className={recMode===m.value? 'rec-btn styled active':'rec-btn styled'}
+                onClick={() => setRecMode(m.value)}
+                type="button"
+                style={{
+                  borderRadius: 20,
+                  padding: '4px 18px',
+                  border: recMode===m.value ? '2px solid #e84c4a' : '1px solid #ccc',
+                  background: recMode===m.value ? '#fff0f0' : '#fff',
+                  color: recMode===m.value ? '#e84c4a' : '#333',
+                  fontWeight: recMode===m.value ? 600 : 400,
+                  cursor: 'pointer',
+                  transition: 'all 0.2s',
+                  outline: 'none',
+                }}
+              >{m.label}</button>
+            ))}
+          </div>
+          {/* 协同过滤推荐数量选择 */}
+          {recMode === 'cf' && (
+            <div style={{display:'flex',alignItems:'center',gap:4}}>
+              <span>推荐数量：</span>
+              <select value={recCFNum} onChange={e => { setRecCFNum(Number(e.target.value)); fetchCFRecommend(Number(e.target.value)) }} style={{padding:'2px 8px',borderRadius:6,border:'1px solid #ccc'}}>
+                {[10, 20, 30, 50].map(n => <option key={n} value={n}>{n}</option>)}
+              </select>
+            </div>
+          )}
+        </div>
+      )}
+
+      {/* 搜索栏 */}
+      <form className="feed-search" onSubmit={handleSearch} style={{marginBottom:16, display:'flex', gap:8, alignItems:'center'}}>
+        <input
+          type="text"
+          className="search-input"
+          placeholder="搜索内容/标题/标签"
+          value={search}
+          onChange={e => setSearch(e.target.value)}
+        />
+        <button type="submit" className="search-btn">搜索</button>
+      </form>
+
       {/* 顶部分类 */}
       <nav className="feed-tabs">
         {categories.map(cat => (
           <button
             key={cat}
             className={cat === activeCat ? 'tab active' : 'tab'}
-            onClick={() => setActiveCat(cat)}
+            onClick={() => { 
+              setActiveCat(cat); 
+              setSearch('');
+              if (useSearchRecommend) {
+                if (cat === '推荐') {
+                  fetchUserTagsAndRecommend()
+                } else {
+                  fetchSearchContent()
+                }
+              }
+            }}
           >
             {cat}
           </button>
         ))}
-      </nav>
-
-      {/* 状态提示 */}
+      </nav>      {/* 状态提示 */}
       {loading ? (
         <div className="loading">加载中…</div>
       ) : error ? (
@@ -69,22 +299,27 @@
       ) : (
         /* 瀑布流卡片区 */
         <div className="feed-grid">
-          {items.map(item => (
-            <div key={item.id} className="feed-card">
-              <img className="card-img" src={item.img} alt={item.title} />
-              <h3 className="card-title">{item.title}</h3>
-              <div className="card-footer">
-                <div className="card-author">
-                  <img className="avatar" src={item.avatar} alt={item.author} />
-                  <span className="username">{item.author}</span>
-                </div>
-                <div className="card-likes">
-                  <ThumbsUp size={16} />
-                  <span className="likes-count">{item.likes}</span>
+          {items.length === 0 ? (
+            <div style={{padding:32, color:'#aaa'}}>暂无内容</div>
+          ) : (
+            items.map(item => (
+              <div key={item.id} className="feed-card" onClick={() => handlePostClick(item.id)}>
+                {item.img && <img className="card-img" src={item.img} alt={item.title} />}
+                <h3 className="card-title">{item.title}</h3>
+                {item.content && <div className="card-content">{item.content.slice(0, 60) || ''}</div>}
+                <div className="card-footer">
+                  <div className="card-author">
+                    <img className="avatar" src={item.avatar} alt={item.author} />
+                    <span className="username">{item.author}</span>
+                  </div>
+                  <div className="card-likes">
+                    <ThumbsUp size={16} />
+                    <span className="likes-count">{item.likes}</span>
+                  </div>
                 </div>
               </div>
-            </div>
-          ))}
+            ))
+          )}
         </div>
       )}
     </div>
diff --git a/Merge/front/src/components/LogsDashboard.js b/Merge/front/src/components/LogsDashboard.js
index 22047e2..6ab4746 100644
--- a/Merge/front/src/components/LogsDashboard.js
+++ b/Merge/front/src/components/LogsDashboard.js
@@ -3,7 +3,9 @@
 import '../style/Admin.css';
 
 function LogsDashboard() {
+  // eslint-disable-next-line no-unused-vars
   const [logs, setLogs] = useState([]);
+  // eslint-disable-next-line no-unused-vars
   const [stats, setStats] = useState({});
 
   useEffect(() => {
diff --git a/Merge/front/src/components/PostDetailJWLLL.jsx b/Merge/front/src/components/PostDetailJWLLL.jsx
new file mode 100644
index 0000000..0dc7289
--- /dev/null
+++ b/Merge/front/src/components/PostDetailJWLLL.jsx
@@ -0,0 +1,322 @@
+import React, { useState, useEffect } from 'react'
+import { useParams, useNavigate } from 'react-router-dom'
+import { ArrowLeft, ThumbsUp, MessageCircle, Share2, BookmarkPlus, Heart, Eye } from 'lucide-react'
+import { searchAPI } from '../api/search_jwlll'
+import '../style/PostDetail.css'
+
+export default function PostDetail() {
+  const { id } = useParams()
+  const navigate = useNavigate()
+  const [post, setPost] = useState(null)
+  const [loading, setLoading] = useState(true)
+  const [error, setError] = useState(null)
+  const [liked, setLiked] = useState(false)
+  const [bookmarked, setBookmarked] = useState(false)
+  const [likeCount, setLikeCount] = useState(0)
+  const [comments, setComments] = useState([])
+  const [newComment, setNewComment] = useState('')
+  const [showComments, setShowComments] = useState(false)
+
+  const DEFAULT_USER_ID = '3' // 默认用户ID
+
+  useEffect(() => {
+    fetchPostDetail()
+    fetchComments()
+  }, [id])
+
+  const fetchPostDetail = async () => {
+    setLoading(true)
+    setError(null)
+    try {
+      const data = await searchAPI.getPostDetail(id)
+      setPost(data)
+      setLikeCount(data.heat || 0)
+    } catch (error) {
+      console.error('获取帖子详情失败:', error)
+      setError('帖子不存在或已被删除')
+    } finally {
+      setLoading(false)
+    }
+  }
+
+  const fetchComments = async () => {
+    try {
+      const data = await searchAPI.getComments(id)
+      setComments(data.comments || [])
+    } catch (error) {
+      console.error('获取评论失败:', error)
+    }
+  }
+
+  const handleBack = () => {
+    navigate(-1)
+  }
+
+  const handleLike = async () => {
+    try {
+      const newLiked = !liked
+      if (newLiked) {
+        await searchAPI.likePost(id, DEFAULT_USER_ID)
+      } else {
+        await searchAPI.unlikePost(id, DEFAULT_USER_ID)
+      }
+      setLiked(newLiked)
+      setLikeCount(prev => newLiked ? prev + 1 : prev - 1)
+    } catch (error) {
+      console.error('点赞失败:', error)
+      // 回滚状态
+      setLiked(!liked)
+      setLikeCount(prev => liked ? prev + 1 : prev - 1)
+    }
+  }
+
+  const handleBookmark = () => {
+    setBookmarked(!bookmarked)
+    // 实际项目中这里应该调用后端API保存收藏状态
+  }
+
+  const handleShare = () => {
+    // 分享功能
+    if (navigator.share) {
+      navigator.share({
+        title: post?.title,
+        text: post?.content,
+        url: window.location.href,
+      })
+    } else {
+      // 复制链接到剪贴板
+      navigator.clipboard.writeText(window.location.href)
+      alert('链接已复制到剪贴板')
+    }
+  }
+
+  const handleAddComment = async (e) => {
+    e.preventDefault()
+    if (!newComment.trim()) return
+
+    try {
+      await searchAPI.addComment(id, DEFAULT_USER_ID, newComment)
+      setNewComment('')
+      fetchComments() // 刷新评论列表
+    } catch (error) {
+      console.error('添加评论失败:', error)
+      alert('评论失败，请重试')
+    }
+  }
+
+  if (loading) {
+    return (
+      <div className="post-detail">
+        <div className="loading-container">
+          <div className="loading-spinner"></div>
+          <p>加载中...</p>
+        </div>
+      </div>
+    )
+  }
+
+  if (error) {
+    return (
+      <div className="post-detail">
+        <div className="error-container">
+          <h2>😔 出错了</h2>
+          <p>{error}</p>
+          <button onClick={handleBack} className="back-btn">
+            <ArrowLeft size={20} />
+            返回
+          </button>
+        </div>
+      </div>
+    )
+  }
+
+  if (!post) {
+    return (
+      <div className="post-detail">
+        <div className="error-container">
+          <h2>😔 帖子不存在</h2>
+          <p>该帖子可能已被删除或不存在</p>
+          <button onClick={handleBack} className="back-btn">
+            <ArrowLeft size={20} />
+            返回
+          </button>
+        </div>
+      </div>
+    )
+  }
+
+  return (
+    <div className="post-detail">
+      {/* 顶部导航栏 */}
+      <header className="post-header">
+        <button onClick={handleBack} className="back-btn">
+          <ArrowLeft size={20} />
+          返回
+        </button>
+        <div className="header-actions">
+          <button onClick={handleShare} className="action-btn">
+            <Share2 size={20} />
+          </button>
+          <button 
+            onClick={handleBookmark} 
+            className={`action-btn ${bookmarked ? 'active' : ''}`}
+          >
+            <BookmarkPlus size={20} />
+          </button>
+        </div>
+      </header>
+
+      {/* 主要内容区 */}
+      <main className="post-content">
+        {/* 帖子标题 */}
+        <h1 className="post-title">{post.title}</h1>
+
+        {/* 作者信息和元数据 */}
+        <div className="post-meta">
+          <div className="author-info">
+            <div className="avatar">
+              {post.author ? post.author.charAt(0).toUpperCase() : 'U'}
+            </div>
+            <div className="author-details">
+              <span className="author-name">{post.author || '匿名用户'}</span>
+              <span className="post-date">
+                {post.create_time ? new Date(post.create_time).toLocaleDateString('zh-CN') : '未知时间'}
+              </span>
+            </div>
+          </div>
+          <div className="post-stats">
+            <span className="stat-item">
+              <Eye size={16} />
+              {post.views || 0}
+            </span>
+            <span className="stat-item">
+              <Heart size={16} />
+              {likeCount}
+            </span>
+          </div>
+        </div>
+
+        {/* 标签 */}
+        {post.tags && post.tags.length > 0 && (
+          <div className="post-tags">
+            {post.tags.map((tag, index) => (
+              <span key={index} className="tag">{tag}</span>
+            ))}
+          </div>
+        )}
+
+        {/* 帖子正文 */}
+        <div className="post-body">
+          <p>{post.content}</p>
+        </div>
+
+        {/* 类别信息 */}
+        {(post.category || post.type) && (
+          <div className="post-category">
+            {post.category && (
+              <>
+                <span className="category-label">分类：</span>
+                <span className="category-name">{post.category}</span>
+              </>
+            )}
+            {post.type && (
+              <>
+                <span className="category-label" style={{marginLeft: '1em'}}>类型：</span>
+                <span className="category-name">{post.type}</span>
+              </>
+            )}
+          </div>
+        )}
+
+        {/* 评论区 */}
+        <div className="comments-section">
+          <div className="comments-header">
+            <button 
+              onClick={() => setShowComments(!showComments)}
+              className="comments-toggle"
+            >
+              <MessageCircle size={20} />
+              评论 ({comments.length})
+            </button>
+          </div>
+
+          {showComments && (
+            <div className="comments-content">
+              {/* 添加评论 */}
+              <form onSubmit={handleAddComment} className="comment-form">
+                <textarea
+                  value={newComment}
+                  onChange={(e) => setNewComment(e.target.value)}
+                  placeholder="写下你的评论..."
+                  className="comment-input"
+                  rows={3}
+                />
+                <button type="submit" className="comment-submit">
+                  发布评论
+                </button>
+              </form>
+
+              {/* 评论列表 */}
+              <div className="comments-list">
+                {comments.length === 0 ? (
+                  <p className="no-comments">暂无评论</p>
+                ) : (
+                  comments.map((comment, index) => (
+                    <div key={index} className="comment-item">
+                      <div className="comment-author">
+                        <div className="comment-avatar">
+                          {comment.user_name ? comment.user_name.charAt(0).toUpperCase() : 'U'}
+                        </div>
+                        <span className="comment-name">{comment.user_name || '匿名用户'}</span>
+                        <span className="comment-time">
+                          {comment.create_time ? new Date(comment.create_time).toLocaleString('zh-CN') : ''}
+                        </span>
+                      </div>
+                      <div className="comment-content">
+                        {comment.content}
+                      </div>
+                    </div>
+                  ))
+                )}
+              </div>
+            </div>
+          )}
+        </div>
+      </main>
+
+      {/* 底部操作栏 */}
+      <footer className="post-footer">
+        <div className="action-bar">
+          <button 
+            onClick={handleLike} 
+            className={`action-button ${liked ? 'liked' : ''}`}
+          >
+            <ThumbsUp size={20} />
+            <span>{likeCount}</span>
+          </button>
+          
+          <button 
+            onClick={() => setShowComments(!showComments)}
+            className="action-button"
+          >
+            <MessageCircle size={20} />
+            <span>评论</span>
+          </button>
+          
+          <button onClick={handleShare} className="action-button">
+            <Share2 size={20} />
+            <span>分享</span>
+          </button>
+          
+          <button 
+            onClick={handleBookmark} 
+            className={`action-button ${bookmarked ? 'bookmarked' : ''}`}
+          >
+            <BookmarkPlus size={20} />
+            <span>收藏</span>
+          </button>
+        </div>
+      </footer>
+    </div>
+  )
+}
diff --git a/Merge/front/src/components/Sidebar.jsx b/Merge/front/src/components/Sidebar.jsx
index 92bc8f1..e35db63 100644
--- a/Merge/front/src/components/Sidebar.jsx
+++ b/Merge/front/src/components/Sidebar.jsx
@@ -7,6 +7,8 @@
   Activity,
   Users,
   ChevronDown,
+  Search,
+  Upload,
 } from 'lucide-react'
 import '../App.css'
 
@@ -24,6 +26,7 @@
       { id: 'fans',     label: '粉丝数据', path: '/dashboard/fans'     },
     ]
   },
+  { id: 'upload-jwlll', label: '智能发布', icon: Upload, path: '/upload-jwlll' },
   // { id: 'activity', label: '活动中心', icon: Activity, path: '/activity' },
   // { id: 'notes',    label: '笔记灵感', icon: BookOpen, path: '/notes'    },
   // { id: 'creator',  label: '创作学院', icon: Users,    path: '/creator'  },
diff --git a/Merge/front/src/components/UploadPageJWLLL.jsx b/Merge/front/src/components/UploadPageJWLLL.jsx
new file mode 100644
index 0000000..2d9ee7d
--- /dev/null
+++ b/Merge/front/src/components/UploadPageJWLLL.jsx
@@ -0,0 +1,328 @@
+import React, { useState } from 'react'
+import { Image, Video, Send } from 'lucide-react'
+import { searchAPI } from '../api/search_jwlll'
+import '../style/UploadPage.css'
+
+const categories = [
+  '穿搭','美食','彩妆','影视',
+  '职场','情感','家居','游戏','旅行','健身'
+]
+
+export default function UploadPageJWLLL({ onComplete }) {
+  const [activeTab, setActiveTab] = useState('image')
+  const [isDragOver, setIsDragOver] = useState(false)
+  const [isUploading, setIsUploading] = useState(false)
+  const [uploadedFiles, setUploadedFiles] = useState([])
+  const [uploadProgress, setUploadProgress] = useState(0)
+  
+  // 新增表单字段
+  const [title, setTitle] = useState('')
+  const [content, setContent] = useState('')
+  const [tags, setTags] = useState('')
+  const [category, setCategory] = useState(categories[0])
+  const [isPublishing, setIsPublishing] = useState(false)
+
+  const DEFAULT_USER_ID = '3' // 默认用户ID
+
+  const validateFiles = files => {
+    const imgTypes = ['image/jpeg','image/jpg','image/png','image/webp']
+    const vidTypes = ['video/mp4','video/mov','video/avi']
+    const types = activeTab==='video'? vidTypes : imgTypes
+    const max   = activeTab==='video'? 2*1024*1024*1024 : 32*1024*1024
+
+    const invalid = files.filter(f => !types.includes(f.type) || f.size > max)
+    if (invalid.length) {
+      alert(`发现 ${invalid.length} 个无效文件，请检查文件格式和大小`)
+      return false
+    }
+    return true
+  }
+
+  const simulateUpload = files => {
+    setIsUploading(true)
+    setUploadProgress(0)
+    setUploadedFiles(files)
+    const iv = setInterval(() => {
+      setUploadProgress(p => {
+        if (p >= 100) {
+          clearInterval(iv)
+          setIsUploading(false)
+          if (typeof onComplete === 'function') {
+            onComplete(files)
+          }
+          return 100
+        }
+        return p + 10
+      })
+    }, 200)
+  }
+
+  const handleFileUpload = () => {
+    if (isUploading) return
+    const input = document.createElement('input')
+    input.type = 'file'
+    input.accept = activeTab==='video'? 'video/*' : 'image/*'
+    input.multiple = activeTab==='image'
+    input.onchange = e => {
+      const files = Array.from(e.target.files)
+      if (files.length > 0 && validateFiles(files)) simulateUpload(files)
+    }
+    input.click()
+  }
+
+  const handleDragOver  = e => { e.preventDefault(); e.stopPropagation(); setIsDragOver(true) }
+  const handleDragLeave = e => { e.preventDefault(); e.stopPropagation(); setIsDragOver(false) }
+  const handleDrop      = e => {
+    e.preventDefault(); e.stopPropagation(); setIsDragOver(false)
+    if (isUploading) return
+    const files = Array.from(e.dataTransfer.files)
+    if (files.length > 0 && validateFiles(files)) simulateUpload(files)
+  }
+
+  const clearFiles = () => setUploadedFiles([])
+  const removeFile = idx => setUploadedFiles(f => f.filter((_,i) => i!==idx))
+
+  // 发布帖子
+  const handlePublish = async () => {
+    if (!title.trim()) {
+      alert('请输入标题')
+      return
+    }
+    if (!content.trim()) {
+      alert('请输入内容')
+      return
+    }
+
+    setIsPublishing(true)
+    try {
+      const postData = {
+        user_id: DEFAULT_USER_ID,
+        title: title.trim(),
+        content: content.trim(),
+        tags: tags.split(',').map(t => t.trim()).filter(t => t),
+        category: category,
+        type: activeTab === 'video' ? 'video' : 'image',
+        media_files: uploadedFiles.map(f => f.name) // 实际项目中应该是上传后的URL
+      }
+
+      await searchAPI.uploadPost(postData)
+      alert('发布成功！')
+      
+      // 清空表单
+      setTitle('')
+      setContent('')
+      setTags('')
+      setUploadedFiles([])
+      setActiveTab('image')
+      
+    } catch (error) {
+      console.error('发布失败:', error)
+      alert('发布失败，请重试')
+    } finally {
+      setIsPublishing(false)
+    }
+  }
+
+  return (
+    <div className="upload-page-jwlll">
+      <div className="upload-tabs">
+        <button
+          className={`upload-tab${activeTab==='video'?' active':''}`}
+          onClick={() => setActiveTab('video')}
+        >上传视频</button>
+        <button
+          className={`upload-tab${activeTab==='image'?' active':''}`}
+          onClick={() => setActiveTab('image')}
+        >上传图文</button>
+      </div>
+
+      {/* 内容表单 */}
+      <div className="content-form">
+        <div className="form-group">
+          <label htmlFor="title">标题</label>
+          <input
+            id="title"
+            type="text"
+            value={title}
+            onChange={(e) => setTitle(e.target.value)}
+            placeholder="请输入标题..."
+            className="form-input"
+            maxLength={100}
+          />
+        </div>
+
+        <div className="form-group">
+          <label htmlFor="content">内容</label>
+          <textarea
+            id="content"
+            value={content}
+            onChange={(e) => setContent(e.target.value)}
+            placeholder="请输入内容..."
+            className="form-textarea"
+            rows={4}
+            maxLength={1000}
+          />
+        </div>
+
+        <div className="form-row">
+          <div className="form-group">
+            <label htmlFor="category">分类</label>
+            <select
+              id="category"
+              value={category}
+              onChange={(e) => setCategory(e.target.value)}
+              className="form-select"
+            >
+              {categories.map(cat => (
+                <option key={cat} value={cat}>{cat}</option>
+              ))}
+            </select>
+          </div>
+
+          <div className="form-group">
+            <label htmlFor="tags">标签</label>
+            <input
+              id="tags"
+              type="text"
+              value={tags}
+              onChange={(e) => setTags(e.target.value)}
+              placeholder="用逗号分隔多个标签..."
+              className="form-input"
+            />
+          </div>
+        </div>
+      </div>
+
+      {/* 文件上传区域 */}
+      <div
+        className={`upload-area${isDragOver?' drag-over':''}`}
+        onDragOver={handleDragOver}
+        onDragLeave={handleDragLeave}
+        onDrop={handleDrop}
+      >
+        <div className="upload-icon">
+          {activeTab==='video'? <Video/> : <Image/>}
+        </div>
+        <h2 className="upload-title">
+          {activeTab==='video'
+            ? '拖拽视频到此处或点击上传'
+            : '拖拽图片到此处或点击上传'
+          }
+        </h2>
+        <p className="upload-subtitle">(需支持上传格式)</p>
+        <button
+          className={`upload-btn${isUploading?' uploading':''}`}
+          onClick={handleFileUpload}
+          disabled={isUploading}
+        >
+          {isUploading
+            ? `上传中... ${uploadProgress}%`
+            : activeTab==='video'
+              ? '上传视频'
+              : '上传图片'
+          }
+        </button>
+
+        {isUploading && (
+          <div className="progress-container">
+            <div className="progress-bar">
+              <div
+                className="progress-fill"
+                style={{ width: `${uploadProgress}%` }}
+              />
+            </div>
+            <div className="progress-text">{uploadProgress}%</div>
+          </div>
+        )}
+      </div>
+
+      {uploadedFiles.length > 0 && (
+        <div className="file-preview-area">
+          <div className="preview-header">
+            <h3 className="preview-title">已上传文件 ({uploadedFiles.length})</h3>
+            <button className="clear-files-btn" onClick={clearFiles}>
+              清除所有
+            </button>
+          </div>
+          <div className="file-grid">
+            {uploadedFiles.map((file, i) => (
+              <div key={i} className="file-item">
+                <button
+                  className="remove-file-btn"
+                  onClick={() => removeFile(i)}
+                  title="删除文件"
+                >×</button>
+                {file.type.startsWith('image/') ? (
+                  <div className="file-thumbnail">
+                    <img src={URL.createObjectURL(file)} alt={file.name} />
+                  </div>
+                ) : (
+                  <div className="file-thumbnail video-thumbnail">
+                    <Video size={24} />
+                  </div>
+                )}
+                <div className="file-info">
+                  <div className="file-name" title={file.name}>
+                    {file.name.length > 20
+                      ? file.name.slice(0,17) + '...'
+                      : file.name
+                    }
+                  </div>
+                  <div className="file-size">
+                    {(file.size/1024/1024).toFixed(2)} MB
+                  </div>
+                </div>
+              </div>
+            ))}
+          </div>
+        </div>
+      )}
+
+      {/* 发布按钮 */}
+      <div className="publish-section">
+        <button
+          className={`publish-btn${isPublishing?' publishing':''}`}
+          onClick={handlePublish}
+          disabled={isPublishing || !title.trim() || !content.trim()}
+        >
+          <Send size={20} />
+          {isPublishing ? '发布中...' : '发布'}
+        </button>
+      </div>
+
+      <div className="upload-info fade-in">
+        {activeTab==='image' ? (
+          <>
+            <div className="info-item">
+              <h3 className="info-title">图片大小</h3>
+              <p className="info-desc">最大32MB</p>
+            </div>
+            <div className="info-item">
+              <h3 className="info-title">图片格式</h3>
+              <p className="info-desc">png/jpg/jpeg/webp</p>
+            </div>
+            <div className="info-item">
+              <h3 className="info-title">分辨率</h3>
+              <p className="info-desc">建议720×960及以上</p>
+            </div>
+          </>
+        ) : (
+          <>
+            <div className="info-item">
+              <h3 className="info-title">视频大小</h3>
+              <p className="info-desc">最大2GB，时长≤5分钟</p>
+            </div>
+            <div className="info-item">
+              <h3 className="info-title">视频格式</h3>
+              <p className="info-desc">mp4/mov</p>
+            </div>
+            <div className="info-item">
+              <h3 className="info-title">分辨率</h3>
+              <p className="info-desc">建议720P及以上</p>
+            </div>
+          </>
+        )}
+      </div>
+    </div>
+  )
+}
diff --git a/Merge/front/src/router/App.js b/Merge/front/src/router/App.js
index d7f5f09..efc4067 100644
--- a/Merge/front/src/router/App.js
+++ b/Merge/front/src/router/App.js
@@ -12,6 +12,8 @@
 import NotebookPage       from '../components/NotebookPage'
 import PlaceholderPage    from '../components/PlaceholderPage'
 import UserProfile        from '../components/UserProfile'
+import PostDetailJWLLL    from '../components/PostDetailJWLLL'
+import UploadPageJWLLL    from '../components/UploadPageJWLLL'
 
 import AdminPage          from '../components/Admin'
 import SuperAdmin         from '../components/SuperAdmin'
@@ -41,13 +43,13 @@
         {/* 2.1 任何登录用户都能看自己的主页 */}
         <Route element={<RequireOwnProfile />}>
           <Route path="/user/:userId" element={<UserProfile />} />
-        </Route>
-
-        {/* 2.2 普通用户 */}
+        </Route>        {/* 2.2 普通用户 */}
         <Route element={<RequireRole allowedRoles={['user']} />}>
           <Route path="/home"               element={<HomeFeed />} />
+          <Route path="/post/:id"           element={<PostDetailJWLLL />} />
           <Route path="/posts/new"          element={<CreatePost />} />
           <Route path="/posts/edit/:postId" element={<CreatePost />} />
+          <Route path="/upload-jwlll"       element={<UploadPageJWLLL />} />
           <Route path="/notebooks"          element={<NotebookPage />} />
           <Route path="/dashboard/*"        element={<PlaceholderPage />} />
           <Route path="/activity"           element={<PlaceholderPage pageId="activity" />} />
diff --git a/Merge/front/src/style/HomeFeed.css b/Merge/front/src/style/HomeFeed.css
index f1bf75d..394c84b 100644
--- a/Merge/front/src/style/HomeFeed.css
+++ b/Merge/front/src/style/HomeFeed.css
@@ -113,4 +113,95 @@
 .card-likes .likes-count {
   font-size: 13px;
   color: #666;
+}
+
+/* --------- JWLLL 搜索推荐功能样式 --------- */
+
+/* 搜索框美化 */
+.feed-search {
+  display: flex;
+  gap: 8px;
+  align-items: center;
+}
+
+.search-input {
+  flex: 1;
+  padding: 8px 14px;
+  border: 1.5px solid #e0e0e0;
+  border-radius: 20px;
+  font-size: 15px;
+  outline: none;
+  transition: border 0.2s;
+  background: #fafbfc;
+}
+
+.search-input:focus {
+  border: 1.5px solid #e84c4a;
+  background: #fff;
+}
+
+.search-btn {
+  padding: 8px 22px;
+  border: none;
+  border-radius: 20px;
+  background: linear-gradient(90deg,#ff6a6a,#ff4757);
+  color: #fff;
+  font-weight: 600;
+  font-size: 15px;
+  cursor: pointer;
+  box-shadow: 0 2px 8px rgba(255,71,87,0.08);
+  transition: background 0.2s, box-shadow 0.2s;
+}
+
+.search-btn:hover {
+  background: linear-gradient(90deg,#ff4757,#e84c4a);
+  box-shadow: 0 4px 16px rgba(255,71,87,0.15);
+}
+
+/* 推荐模式切换按钮 */
+.rec-btn {
+  background: #f8f9fa;
+  border: 1px solid #dee2e6;
+  color: #495057;
+  padding: 6px 12px;
+  border-radius: 20px;
+  cursor: pointer;
+  transition: all 0.2s;
+  font-size: 14px;
+  outline: none;
+}
+
+.rec-btn:hover {
+  background: #e9ecef;
+}
+
+.rec-btn.active {
+  background: #fff0f0;
+  border: 2px solid #e84c4a;
+  color: #e84c4a;
+  font-weight: 600;
+}
+
+/* 卡片内容显示区域 */
+.card-content {
+  padding: 0 12px 8px;
+  font-size: 13px;
+  color: #666;
+  line-height: 1.4;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  display: -webkit-box;
+  -webkit-line-clamp: 2;
+  -webkit-box-orient: vertical;
+}
+
+/* 点击效果 */
+.feed-card {
+  cursor: pointer;
+  transition: transform 0.2s, box-shadow 0.2s;
+}
+
+.feed-card:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 4px 12px rgba(0,0,0,0.15);
 }
\ No newline at end of file
diff --git a/Merge/front/src/style/PostDetail.css b/Merge/front/src/style/PostDetail.css
new file mode 100644
index 0000000..1be7126
--- /dev/null
+++ b/Merge/front/src/style/PostDetail.css
@@ -0,0 +1,436 @@
+/* 帖子详情页面容器 */
+.post-detail {
+  max-width: 800px;
+  margin: 0 auto;
+  background: #fff;
+  min-height: 100vh;
+  display: flex;
+  flex-direction: column;
+}
+
+/* 加载状态 */
+.loading-container {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  padding: 60px 20px;
+  color: #666;
+}
+
+.loading-spinner {
+  width: 40px;
+  height: 40px;
+  border: 3px solid #f3f3f3;
+  border-top: 3px solid #ff4757;
+  border-radius: 50%;
+  animation: spin 1s linear infinite;
+  margin-bottom: 20px;
+}
+
+@keyframes spin {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+/* 错误状态 */
+.error-container {
+  text-align: center;
+  padding: 60px 20px;
+  color: #666;
+}
+
+.error-container h2 {
+  margin-bottom: 16px;
+  color: #333;
+}
+
+/* 顶部导航栏 */
+.post-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 16px 20px;
+  border-bottom: 1px solid #eee;
+  background: #fff;
+  position: sticky;
+  top: 0;
+  z-index: 100;
+}
+
+.back-btn {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 8px 16px;
+  border: none;
+  background: #f8f9fa;
+  border-radius: 20px;
+  cursor: pointer;
+  transition: background-color 0.2s;
+  font-size: 14px;
+  color: #333;
+}
+
+.back-btn:hover {
+  background: #e9ecef;
+}
+
+.header-actions {
+  display: flex;
+  gap: 8px;
+}
+
+.action-btn {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  width: 40px;
+  height: 40px;
+  border: none;
+  background: #f8f9fa;
+  border-radius: 50%;
+  cursor: pointer;
+  transition: background-color 0.2s;
+  color: #666;
+}
+
+.action-btn:hover {
+  background: #e9ecef;
+}
+
+.action-btn.active {
+  background: #ff4757;
+  color: white;
+}
+
+/* 主要内容区 */
+.post-content {
+  flex: 1;
+  padding: 20px;
+}
+
+.post-title {
+  font-size: 24px;
+  font-weight: 700;
+  line-height: 1.4;
+  margin-bottom: 20px;
+  color: #333;
+}
+
+/* 帖子元信息 */
+.post-meta {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 20px;
+  padding-bottom: 16px;
+  border-bottom: 1px solid #f0f0f0;
+}
+
+.author-info {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+}
+
+.avatar {
+  width: 40px;
+  height: 40px;
+  border-radius: 50%;
+  background: #ff4757;
+  color: white;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-weight: 600;
+  font-size: 16px;
+}
+
+.author-details {
+  display: flex;
+  flex-direction: column;
+  gap: 2px;
+}
+
+.author-name {
+  font-weight: 600;
+  color: #333;
+  font-size: 14px;
+}
+
+.post-date {
+  font-size: 12px;
+  color: #666;
+}
+
+.post-stats {
+  display: flex;
+  gap: 16px;
+}
+
+.stat-item {
+  display: flex;
+  align-items: center;
+  gap: 4px;
+  font-size: 14px;
+  color: #666;
+}
+
+/* 标签 */
+.post-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  margin-bottom: 20px;
+}
+
+.tag {
+  padding: 4px 12px;
+  background: #f8f9fa;
+  border-radius: 16px;
+  font-size: 12px;
+  color: #666;
+  border: 1px solid #e9ecef;
+}
+
+/* 帖子正文 */
+.post-body {
+  margin-bottom: 24px;
+  line-height: 1.6;
+  color: #333;
+  font-size: 16px;
+}
+
+.post-body p {
+  margin-bottom: 16px;
+}
+
+/* 类别信息 */
+.post-category {
+  margin-bottom: 20px;
+  padding: 12px;
+  background: #f8f9fa;
+  border-radius: 8px;
+  font-size: 14px;
+}
+
+.category-label {
+  color: #666;
+  font-weight: 500;
+}
+
+.category-name {
+  color: #333;
+  font-weight: 600;
+}
+
+/* 评论区 */
+.comments-section {
+  margin-top: 32px;
+  padding-top: 24px;
+  border-top: 1px solid #eee;
+}
+
+.comments-header {
+  margin-bottom: 20px;
+}
+
+.comments-toggle {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 16px;
+  border: 1px solid #e9ecef;
+  background: #f8f9fa;
+  border-radius: 8px;
+  cursor: pointer;
+  transition: background-color 0.2s;
+  font-size: 14px;
+  color: #333;
+}
+
+.comments-toggle:hover {
+  background: #e9ecef;
+}
+
+.comments-content {
+  margin-top: 16px;
+}
+
+/* 评论表单 */
+.comment-form {
+  margin-bottom: 24px;
+  padding: 16px;
+  background: #f8f9fa;
+  border-radius: 8px;
+}
+
+.comment-input {
+  width: 100%;
+  padding: 12px;
+  border: 1px solid #dee2e6;
+  border-radius: 6px;
+  resize: vertical;
+  font-family: inherit;
+  font-size: 14px;
+  margin-bottom: 12px;
+  outline: none;
+  transition: border-color 0.2s;
+}
+
+.comment-input:focus {
+  border-color: #ff4757;
+}
+
+.comment-submit {
+  padding: 8px 16px;
+  background: #ff4757;
+  color: white;
+  border: none;
+  border-radius: 6px;
+  cursor: pointer;
+  font-size: 14px;
+  transition: background-color 0.2s;
+}
+
+.comment-submit:hover {
+  background: #e84c4a;
+}
+
+/* 评论列表 */
+.comments-list {
+  display: flex;
+  flex-direction: column;
+  gap: 16px;
+}
+
+.no-comments {
+  text-align: center;
+  color: #666;
+  font-style: italic;
+  padding: 32px 0;
+}
+
+.comment-item {
+  padding: 16px;
+  background: #fff;
+  border: 1px solid #e9ecef;
+  border-radius: 8px;
+}
+
+.comment-author {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  margin-bottom: 8px;
+}
+
+.comment-avatar {
+  width: 32px;
+  height: 32px;
+  border-radius: 50%;
+  background: #6c757d;
+  color: white;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-weight: 600;
+  font-size: 14px;
+}
+
+.comment-name {
+  font-weight: 600;
+  color: #333;
+  font-size: 14px;
+}
+
+.comment-time {
+  font-size: 12px;
+  color: #666;
+  margin-left: auto;
+}
+
+.comment-content {
+  font-size: 14px;
+  line-height: 1.5;
+  color: #333;
+  margin-left: 40px;
+}
+
+/* 底部操作栏 */
+.post-footer {
+  padding: 16px 20px;
+  border-top: 1px solid #eee;
+  background: #fff;
+  position: sticky;
+  bottom: 0;
+}
+
+.action-bar {
+  display: flex;
+  justify-content: space-around;
+  align-items: center;
+  max-width: 400px;
+  margin: 0 auto;
+}
+
+.action-button {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 4px;
+  padding: 8px 12px;
+  border: none;
+  background: transparent;
+  cursor: pointer;
+  transition: color 0.2s;
+  color: #666;
+  font-size: 12px;
+}
+
+.action-button:hover {
+  color: #333;
+}
+
+.action-button.liked {
+  color: #ff4757;
+}
+
+.action-button.bookmarked {
+  color: #ffa502;
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+  .post-detail {
+    margin: 0;
+  }
+  
+  .post-content {
+    padding: 16px;
+  }
+  
+  .post-title {
+    font-size: 20px;
+  }
+  
+  .post-meta {
+    flex-direction: column;
+    align-items: flex-start;
+    gap: 12px;
+  }
+  
+  .post-stats {
+    align-self: flex-end;
+  }
+  
+  .action-bar {
+    max-width: none;
+  }
+  
+  .comment-content {
+    margin-left: 0;
+    margin-top: 8px;
+  }
+}