diff --git a/model_pro/BERT_CTM.py b/model_pro/BERT_CTM.py
index 8bf1c98..61128b0 100644
--- a/model_pro/BERT_CTM.py
+++ b/model_pro/BERT_CTM.py
@@ -8,41 +8,87 @@ from contextualized_topic_models.utils.data_preparation import TopicModelDataPre
 from contextualized_topic_models.models.ctm import CombinedTM
 
 class BERT_CTM_Model:
-    def __init__(self, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=50):
+    def __init__(self, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=50, device=None):
+        # 确定设备 (CPU/GPU)
+        self.device = device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # 检查模型路径是否存在
+        if not os.path.exists(bert_model_path):
+            raise ValueError(f"BERT模型路径不存在: {bert_model_path}")
+        if not os.path.exists(ctm_tokenizer_path):
+            raise ValueError(f"CTM分词器路径不存在: {ctm_tokenizer_path}")
+
         # 加载BERT模型和tokenizer
         self.tokenizer = BertTokenizer.from_pretrained(bert_model_path)
-        self.model = BertModel.from_pretrained(bert_model_path)
+        self.model = BertModel.from_pretrained(bert_model_path).to(self.device)
 
         # 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
+        self.ctm_model = None
 
     def get_bert_embeddings(self, texts):
         """使用BERT模型批量生成文本的嵌入向量"""
         embeddings = []
         for text in tqdm(texts, desc="Processing texts with BERT"):
-            inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80)
+            inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80).to(self.device)
             with torch.no_grad():
                 outputs = self.model(**inputs)
             embeddings.append(outputs.last_hidden_state.cpu().numpy())  # [batch_size, sequence_length, hidden_size]
         return np.vstack(embeddings)
-    
+
     def chinese_tokenize(self, text):
         """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)
+        try:
+            # 分词并准备BOW文本
+            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")
+            # 训练CTM
+            self.ctm_model = CombinedTM(bow_size=len(self.tp.vocab), contextual_size=768, 
+                                        n_components=self.n_components, num_epochs=self.num_epochs)
+            self.ctm_model.fit(training_dataset)
+            print("CTM模型训练完成")
+        except Exception as e:
+            print(f"训练CTM模型时发生错误: {e}")
+
+    def save_model(self, path):
+        """保存训练后的CTM模型"""
+        if self.ctm_model:
+            self.ctm_model.save(path)
+            print(f"CTM模型已保存至: {path}")
+        else:
+            print("未找到已训练的CTM模型，无法保存")
+
+    def load_model(self, path):
+        """加载已保存的CTM模型"""
+        if os.path.exists(path):
+            self.ctm_model = CombinedTM.load(path)
+            print(f"CTM模型已加载自: {path}")
+        else:
+            print(f"无法加载模型，路径不存在: {path}")
 
 if __name__ == "__main__":
-    model = BERT_CTM_Model('./bert_model', './sentence_bert_model')
+    # 设定BERT和CTM模型的路径
+    bert_model_path = './bert_model'
+    ctm_tokenizer_path = './sentence_bert_model'
+    
+    # 初始化模型
+    model = BERT_CTM_Model(bert_model_path, ctm_tokenizer_path)
+
+    # 示例文本
     texts = ["这是第一个文本", "这是第二个文本"]
+    
+    # 训练CTM模型
     model.train_ctm(texts)
+
+    # 保存CTM模型
+    model.save_model('./trained_ctm_model')
+
+    # 加载CTM模型
+    model.load_model('./trained_ctm_model')