diff --git a/fastNLP/modules/encoder/embedding.py b/fastNLP/modules/encoder/embedding.py
index a58668da..c48cb806 100644
--- a/fastNLP/modules/encoder/embedding.py
+++ b/fastNLP/modules/encoder/embedding.py
@@ -180,11 +180,11 @@ class StaticEmbedding(TokenEmbedding):
         的名称。目前支持的embedding包括{`en` 或者 `en-glove-840b-300` : glove.840B.300d, `en-glove-6b-50` : glove.6B.50d,
         `en-word2vec-300` : GoogleNews-vectors-negative300}。第二种情况将自动查看缓存中是否存在该模型，没有的话将自动下载。
     :param requires_grad: 是否需要gradient. 默认为True
-    :param init_method: 如何初始化没有找到的值。可以使用torch.nn.init.*中各种方法。默认使用torch.nn.init.xavier_uniform_
-        。调用该方法时传入一个tensor对象。
-
+    :param init_method: 如何初始化没有找到的值。可以使用torch.nn.init.*中各种方法。调用该方法时传入一个tensor对象。
+    :param normailize: 是否对vector进行normalize，使得每个vector的norm为1。
     """
-    def __init__(self, vocab: Vocabulary, model_dir_or_name: str='en', requires_grad: bool=True, init_method=None):
+    def __init__(self, vocab: Vocabulary, model_dir_or_name: str='en', requires_grad: bool=True, init_method=None,
+                 normalize=False):
         super(StaticEmbedding, self).__init__(vocab)
 
         # 优先定义需要下载的static embedding有哪些。这里估计需要自己搞一个server，
@@ -202,7 +202,8 @@ class StaticEmbedding(TokenEmbedding):
             raise ValueError(f"Cannot recognize {model_dir_or_name}.")
 
         # 读取embedding
-        embedding, hit_flags = self._load_with_vocab(model_path, vocab=vocab, init_method=init_method)
+        embedding, hit_flags = self._load_with_vocab(model_path, vocab=vocab, init_method=init_method,
+                                                      normalize=normalize)
         self.embedding = nn.Embedding(num_embeddings=embedding.shape[0], embedding_dim=embedding.shape[1],
                                       padding_idx=vocab.padding_idx,
                                       max_norm=None, norm_type=2, scale_grad_by_freq=False,
@@ -257,10 +258,7 @@ class StaticEmbedding(TokenEmbedding):
         assert isinstance(vocab, Vocabulary), "Only fastNLP.Vocabulary is supported."
         if not os.path.exists(embed_filepath):
             raise FileNotFoundError("`{}` does not exist.".format(embed_filepath))
-        if init_method is None:
-            init_method = nn.init.xavier_uniform_
         with open(embed_filepath, 'r', encoding='utf-8') as f:
-            found_count = 0
             line = f.readline().strip()
             parts = line.split()
             start_idx = 0
@@ -271,7 +269,8 @@ class StaticEmbedding(TokenEmbedding):
                 dim = len(parts) - 1
                 f.seek(0)
             matrix = torch.zeros(len(vocab), dim)
-            init_method(matrix)
+            if init_method is not None:
+                init_method(matrix)
             hit_flags = np.zeros(len(vocab), dtype=bool)
             for idx, line in enumerate(f, start_idx):
                 try:
@@ -286,7 +285,6 @@ class StaticEmbedding(TokenEmbedding):
                     if word in vocab:
                         index = vocab.to_index(word)
                         matrix[index] = torch.from_numpy(np.fromstring(' '.join(nums), sep=' ', dtype=dtype, count=dim))
-                        found_count += 1
                         hit_flags[index] = True
                 except Exception as e:
                     if error == 'ignore':
@@ -294,7 +292,16 @@ class StaticEmbedding(TokenEmbedding):
                     else:
                         print("Error occurred at the {} line.".format(idx))
                         raise e
+            found_count = sum(hit_flags)
             print("Found {} out of {} words in the pre-training embedding.".format(found_count, len(vocab)))
+            if init_method is None:
+                if len(vocab)-found_count>0 and found_count>0: # 有的没找到
+                    found_vecs = matrix[torch.LongTensor(hit_flags.astype(int)).byte()]
+                    mean = found_vecs.mean(dim=0, keepdim=True)
+                    std = found_vecs.std(dim=0, keepdim=True)
+                    unfound_vec_num = np.sum(hit_flags==False)
+                    unfound_vecs = torch.randn(unfound_vec_num, dim)*std + mean
+                    matrix[torch.LongTensor(hit_flags.astype(int)).eq(0)] = unfound_vecs
 
             if normalize:
                 matrix /= (torch.norm(matrix, dim=1, keepdim=True) + 1e-12)