diff --git a/model_pro/BERT_CTM.py b/model_pro/BERT_CTM.py
index 5aa12ed..3cd29cd 100644
--- a/model_pro/BERT_CTM.py
+++ b/model_pro/BERT_CTM.py
@@ -1,6 +1,8 @@
 import os
 from transformers.models.bert import BertTokenizer, BertModel
 import torch
+from tqdm import tqdm
+import numpy as np
 
 class BERT_CTM_Model:
     def __init__(self, bert_model_path):
@@ -8,15 +10,18 @@ class BERT_CTM_Model:
         self.tokenizer = BertTokenizer.from_pretrained(bert_model_path)
         self.model = BertModel.from_pretrained(bert_model_path)
 
-    def get_bert_embeddings(self, text):
-        """使用BERT模型生成文本的嵌入向量"""
-        inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80)
-        with torch.no_grad():
-            outputs = self.model(**inputs)
-        return outputs.last_hidden_state.cpu().numpy()  # [batch_size, sequence_length, hidden_size]
+    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)
+            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)
 
 if __name__ == "__main__":
     model = BERT_CTM_Model('./bert_model')
-    text = "这是一个测试文本"
-    embedding = model.get_bert_embeddings(text)
-    print(embedding.shape)
+    texts = ["这是第一个文本", "这是第二个文本"]
+    embeddings = model.get_bert_embeddings(texts)
+    print(embeddings.shape)