diff --git a/rhj/backend/app/utils/data_loader.py b/rhj/backend/app/utils/data_loader.py
new file mode 100644
index 0000000..c882a12
--- /dev/null
+++ b/rhj/backend/app/utils/data_loader.py
@@ -0,0 +1,97 @@
+from app.utils.parse_args import args
+from os import path
+from tqdm import tqdm
+import numpy as np
+import scipy.sparse as sp
+import torch
+import networkx as nx
+from copy import deepcopy
+from collections import defaultdict
+import pandas as pd
+
+
+class EdgeListData:
+    def __init__(self, train_file, test_file, phase='pretrain', pre_dataset=None, has_time=True):
+        self.phase = phase
+        self.has_time = has_time
+        self.pre_dataset = pre_dataset
+
+        self.hour_interval = args.hour_interval_pre if phase == 'pretrain' else args.hour_interval_f
+
+        self.edgelist = []
+        self.edge_time = []
+        self.num_users = 0
+        self.num_items = 0
+        self.num_edges = 0
+
+        self.train_user_dict = {}
+        self.test_user_dict = {}
+
+        self._load_data(train_file, test_file, has_time)
+
+        if phase == 'pretrain':
+            self.user_hist_dict = self.train_user_dict
+        
+        users_has_hist = set(list(self.user_hist_dict.keys()))
+        all_users = set(list(range(self.num_users)))
+        users_no_hist = all_users - users_has_hist
+        for u in users_no_hist:
+            self.user_hist_dict[u] = []
+
+    def _read_file(self, train_file, test_file, has_time=True):
+        with open(train_file, 'r') as f:
+            for line in f:
+                line = line.strip().split('\t')
+                if not has_time:
+                    user, items = line[:2]
+                    times = " ".join(["0"] * len(items.split(" ")))
+                    weights = " ".join(["1"] * len(items.split(" "))) if len(line) < 4 else line[3]
+                else:
+                    if len(line) >= 4:  # 包含权重信息
+                        user, items, times, weights = line
+                    else:
+                        user, items, times = line
+                        weights = " ".join(["1"] * len(items.split(" ")))
+                    
+                for i in items.split(" "):
+                    self.edgelist.append((int(user), int(i)))
+                for i in times.split(" "):
+                    self.edge_time.append(int(i))
+                self.train_user_dict[int(user)] = [int(i) for i in items.split(" ")]
+
+        self.test_edge_num = 0
+        with open(test_file, 'r') as f:
+            for line in f:
+                line = line.strip().split('\t')
+                user, items = line[:2]
+                self.test_user_dict[int(user)] = [int(i) for i in items.split(" ")]
+                self.test_edge_num += len(self.test_user_dict[int(user)])
+
+    def _load_data(self, train_file, test_file, has_time=True):
+        self._read_file(train_file, test_file, has_time)
+
+        self.edgelist = np.array(self.edgelist, dtype=np.int32)
+        self.edge_time = 1 + self.timestamp_to_time_step(np.array(self.edge_time, dtype=np.int32))
+        self.num_edges = len(self.edgelist)
+        if self.pre_dataset is not None:
+            self.num_users = self.pre_dataset.num_users
+            self.num_items = self.pre_dataset.num_items
+        else:
+            self.num_users = max([np.max(self.edgelist[:, 0]) + 1, np.max(list(self.test_user_dict.keys())) + 1])
+            self.num_items = max([np.max(self.edgelist[:, 1]) + 1, np.max([np.max(self.test_user_dict[u]) for u in self.test_user_dict.keys()]) + 1])
+
+        self.graph = sp.coo_matrix((np.ones(self.num_edges), (self.edgelist[:, 0], self.edgelist[:, 1])), shape=(self.num_users, self.num_items))
+
+        if self.has_time:
+            self.edge_time_dict = defaultdict(dict)
+            for i in range(len(self.edgelist)):
+                self.edge_time_dict[self.edgelist[i][0]][self.edgelist[i][1]+self.num_users] = self.edge_time[i]
+                self.edge_time_dict[self.edgelist[i][1]+self.num_users][self.edgelist[i][0]] = self.edge_time[i]
+
+    def timestamp_to_time_step(self, timestamp_arr, least_time=None):
+        interval_hour = self.hour_interval
+        if least_time is None:
+            least_time = np.min(timestamp_arr)
+        timestamp_arr = timestamp_arr - least_time
+        timestamp_arr = timestamp_arr // (interval_hour * 3600)
+        return timestamp_arr