diff --git a/fastNLP/embeddings/char_embedding.py b/fastNLP/embeddings/char_embedding.py
index b9e6659e..8243e148 100644
--- a/fastNLP/embeddings/char_embedding.py
+++ b/fastNLP/embeddings/char_embedding.py
@@ -9,6 +9,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from typing import List
 
+from .static_embedding import StaticEmbedding
 from ..modules.encoder.lstm import LSTM
 from ..core.vocabulary import Vocabulary
 from .embedding import TokenEmbedding
@@ -41,10 +42,13 @@ class CNNCharEmbedding(TokenEmbedding):
     :param pool_method: character的表示在合成一个表示时所使用的pool方法，支持'avg', 'max'.
     :param activation: CNN之后使用的激活方法，支持'relu', 'sigmoid', 'tanh' 或者自定义函数.
     :param min_char_freq: character的最少出现次数。默认值为2.
+    :param pre_train_char_embed:可以有两种方式调用预训练好的static embedding：第一种是传入embedding文件夹(文件夹下应该只有一个
+        以.txt作为后缀的文件)或文件路径；第二种是传入embedding的名称，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。
+        如果输入为None则使用embedding_dim的维度随机初始化一个embedding.
     """
     def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0,
                  dropout:float=0.5, filter_nums: List[int]=(40, 30, 20), kernel_sizes: List[int]=(5, 3, 1),
-                 pool_method: str='max', activation='relu', min_char_freq: int=2):
+                 pool_method: str='max', activation='relu', min_char_freq: int=2, pre_train_char_embed: str=''):
         super(CNNCharEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
 
         for kernel in kernel_sizes:
@@ -85,7 +89,11 @@ class CNNCharEmbedding(TokenEmbedding):
             self.words_to_chars_embedding[index, :len(word)] = \
                 torch.LongTensor([self.char_vocab.to_index(c) for c in word])
             self.word_lengths[index] = len(word)
-        self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
+        # self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
+        if len(pre_train_char_embed):
+            self.char_embedding = StaticEmbedding(self.char_vocab, pre_train_char_embed)
+        else:
+            self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
 
         self.convs = nn.ModuleList([nn.Conv1d(
             char_emb_size, filter_nums[i], kernel_size=kernel_sizes[i], bias=True, padding=kernel_sizes[i] // 2)
@@ -184,10 +192,13 @@ class LSTMCharEmbedding(TokenEmbedding):
     :param activation: 激活函数，支持'relu', 'sigmoid', 'tanh', 或者自定义函数.
     :param min_char_freq: character的最小出现次数。默认值为2.
     :param bidirectional: 是否使用双向的LSTM进行encode。默认值为True。
+    :param pre_train_char_embed:可以有两种方式调用预训练好的static embedding：第一种是传入embedding文件夹(文件夹下应该只有一个
+        以.txt作为后缀的文件)或文件路径；第二种是传入embedding的名称，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。
+        如果输入为None则使用embedding_dim的维度随机初始化一个embedding.
     """
     def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0,
                  dropout:float=0.5, hidden_size=50,pool_method: str='max', activation='relu', min_char_freq: int=2,
-                 bidirectional=True):
+                 bidirectional=True, pre_train_char_embed: str=''):
         super(LSTMCharEmbedding, self).__init__(vocab)
 
         assert hidden_size % 2 == 0, "Only even kernel is allowed."
@@ -227,7 +238,11 @@ class LSTMCharEmbedding(TokenEmbedding):
             self.words_to_chars_embedding[index, :len(word)] = \
                 torch.LongTensor([self.char_vocab.to_index(c) for c in word])
             self.word_lengths[index] = len(word)
-        self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
+        # self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
+        if len(pre_train_char_embed):
+            self.char_embedding = StaticEmbedding(self.char_vocab, pre_train_char_embed)
+        else:
+            self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
 
         self.fc = nn.Linear(hidden_size, embed_size)
         hidden_size = hidden_size // 2 if bidirectional else hidden_size