diff --git a/model_pro/BERT_CTM.py b/model_pro/BERT_CTM.py
index 9be30cd..8bf1c98 100644
--- a/model_pro/BERT_CTM.py
+++ b/model_pro/BERT_CTM.py
@@ -4,13 +4,20 @@ import torch
 from tqdm import tqdm
 import numpy as np
 import jieba
+from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
+from contextualized_topic_models.models.ctm import CombinedTM
 
 class BERT_CTM_Model:
-    def __init__(self, bert_model_path):
+    def __init__(self, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=50):
         # 加载BERT模型和tokenizer
         self.tokenizer = BertTokenizer.from_pretrained(bert_model_path)
         self.model = BertModel.from_pretrained(bert_model_path)
 
+        # 创建CTM数据预处理对象
+        self.tp = TopicModelDataPreparation(ctm_tokenizer_path)
+        self.n_components = n_components
+        self.num_epochs = num_epochs
+
     def get_bert_embeddings(self, texts):
         """使用BERT模型批量生成文本的嵌入向量"""
         embeddings = []
@@ -25,8 +32,17 @@ class BERT_CTM_Model:
         """使用jieba对中文文本进行分词"""
         return " ".join(jieba.cut(text))
 
+    def train_ctm(self, texts):
+        """训练CTM模型"""
+        bow_texts = [self.chinese_tokenize(text) for text in texts]
+        training_dataset = self.tp.fit(text_for_contextual=texts, text_for_bow=bow_texts)
+
+        # 训练CTM
+        ctm = CombinedTM(bow_size=len(self.tp.vocab), contextual_size=768, n_components=self.n_components, num_epochs=self.num_epochs)
+        ctm.fit(training_dataset)
+        print("CTM模型训练完成")
+
 if __name__ == "__main__":
-    model = BERT_CTM_Model('./bert_model')
-    text = "这是一个测试文本"
-    tokenized_text = model.chinese_tokenize(text)
-    print(tokenized_text)
+    model = BERT_CTM_Model('./bert_model', './sentence_bert_model')
+    texts = ["这是第一个文本", "这是第二个文本"]
+    model.train_ctm(texts)