diff --git a/recommend/download_model.py b/recommend/download_model.py
index 1bb0c50..b9555dc 100644
--- a/recommend/download_model.py
+++ b/recommend/download_model.py
@@ -1,6 +1,6 @@
 import os
 import urllib.request
-from recommend import train_and_save_itemcf
+
 MODEL_URL = "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300.bin.gz"
 MODEL_DIR = "./models"
 MODEL_PATH = os.path.join(MODEL_DIR, "cc.zh.300.bin")
@@ -28,5 +28,4 @@
     print("✅ 模型下载并解压完成！")
 
 if __name__ == "__main__":
-    train_and_save_itemcf()
     download_model()
diff --git a/recommend/recommend.py b/recommend/recommend.py
index 25032a0..b216d52 100644
--- a/recommend/recommend.py
+++ b/recommend/recommend.py
@@ -15,7 +15,7 @@
 engine = create_engine("mysql+pymysql://sy:sy_password@49.233.215.144:3306/pt_station")
 
 # === ✅ 加载 fastText 模型 ===
-fasttext_model_path = 'E:\\course\\pt\\recommend\\models\\cc.zh.300.bin'
+fasttext_model_path = 'models\\cc.zh.300.bin'
 if not os.path.exists(fasttext_model_path):
     raise FileNotFoundError("fastText 模型文件不存在，请检查路径。")
 print("加载 fastText 模型中...")
@@ -25,7 +25,7 @@
 # === ✅ 用户标签行为矩阵构建 ===
 def get_user_tag_matrix():
     df = pd.read_sql("SELECT user_id, tag, score FROM user_tag_scores", engine)
-    print(df)
+    #print(df)
     df['user_id'] = df['user_id'].astype(str)
     user_map = {u: i for i, u in enumerate(df['user_id'].unique())}
     tag_map = {t: i for i, t in enumerate(df['tag'].unique())}
@@ -39,20 +39,27 @@
 def semantic_recommend(user_id, topn=5):
     print(f"正在为用户 {user_id} 生成推荐...")
 
-    # 读取数据库中的用户标签数据
+    # 读取数据
     df = pd.read_sql("SELECT user_id, tag, score FROM user_tag_scores", engine)
-    print(f"总记录数: {len(df)}")
-    print(f"数据示例:\n{df.head()}")
-    print(df.dtypes)
-    user_id = str(user_id)  # 确保匹配
 
-    # 获取该用户的所有标签（按分数从高到低排序）
+    # 统一类型转换
+    df['user_id'] = df['user_id'].astype(str)  # 确保整个列转为字符串
+    user_id = str(user_id)  # 要查询的ID也转为字符串
+
+    # 现在查询应该正常工作了
     user_tags = df[df['user_id'] == user_id].sort_values(by="score", ascending=False)['tag'].tolist()
     print(f"用户 {user_id} 的标签（按分数排序）: {user_tags}")
 
     if not user_tags:
         print(f"用户 {user_id} 没有标签记录，返回空推荐结果。")
         return []
+    else:
+        user_tags = user_tags[:3]
+        print(f"用户 {user_id} 的 Top 3 标签: {user_tags}")
+
+    if not user_tags:
+        print(f"用户 {user_id} 没有标签记录，返回空推荐结果。")
+        return []
 
     # 截取前 3 个标签作为“兴趣标签”
     user_tags = user_tags[:3]
@@ -85,8 +92,8 @@
     # 排序并返回 topN 标签
     sorted_tags = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:topn]
     print(f"\n最终推荐标签（前 {topn}）:")
-    for tag, score in sorted_tags:
-        print(f"{tag}: {score:.4f}")
+    #for tag, score in sorted_tags:
+      #  print(f"{tag}: {score:.4f}")
 
     return [tag for tag, _ in sorted_tags]
 
@@ -117,8 +124,6 @@
         print(f"⚠️ 用户 {user_id} 没有任何标签评分记录。")
         return []
 
-    print(f"用户 {user_id} 的标签评分:\n{user_tags}")
-
     scores = {}
     for tag, val in user_tags.items():
         if tag not in sim_df:
@@ -212,28 +217,32 @@
 def get_torrent_ids_by_tags(tags, limit_per_tag=10):
     if not tags:
         tags = []
+    print(f"传递给 get_torrent_ids_by_tags 的标签: {tags}")
 
     recommended_ids = set()
     with engine.connect() as conn:
         for tag in tags:
             query = text("""
                 SELECT torrent_id
-                FROM bt_torrent_tags 
-                WHERE tag = :tag 
+                FROM bt_torrent_tags
+                WHERE tag = :tag
                 LIMIT :limit
             """)
             result = conn.execute(query, {"tag": tag, "limit": limit_per_tag})
+            print(f"标签 '{tag}' 的推荐结果:")
             for row in result:
+                print(row[0])  # 打印每个torrent_id
                 recommended_ids.add(row[0])
 
         # 获取数据库中所有 torrent_id
-        all_query = text("SELECT DISTINCT torrent_id FROM bt_torrent_tags")
+        all_query = text("SELECT DISTINCT torrent_id FROM bt_torrent")
         all_result = conn.execute(all_query)
         all_ids = set(row[0] for row in all_result)
+        print("数据库中所有torrent_id:", all_ids)
 
     # 剩下的（非推荐）种子 ID
     remaining_ids = all_ids - recommended_ids
-
+    print(remaining_ids)
     # 随机打乱推荐和剩下的 ID
     recommended_list = list(recommended_ids)
     remaining_list = list(remaining_ids)
diff --git a/recommend/requirements.txt b/recommend/requirements.txt
index 2efe47a..b609f83 100644
--- a/recommend/requirements.txt
+++ b/recommend/requirements.txt
@@ -3,7 +3,7 @@
 huggingface_hub==0.31.2
 jieba==0.42.1
 mysql_connector_repackaged==0.3.1
-numpy==2.2.6
+numpy==1.26.4
 pandas==2.2.3
 scikit_learn==1.6.1
 scikit_surprise==1.1.4
@@ -16,3 +16,4 @@
 torch==2.7.0
 transformers==4.51.3
 waitress==3.0.2
+pymysql
\ No newline at end of file