import jieba

 import re

 import os

 import sys

 import gensim

 import sys  

 f1 =  open(u'copy_train.csv')

 for line in f1.readlines():

     with open(u'process_copy_train2.csv','a') as f2:

         seg_list = jieba.cut(line,cut_all=False)

         seg_list = " ".join(seg_list)

         seg_list.encode("utf8")

         seg_list.decode("utf8")

         f2.write(seg_list)

         f2.write("\n")

         #f2.write(line)

 f2.close()

 f1.close()          

 import jieba

 import re

 import os

 import sys

 import gensim

 import sys 

 from gensim.models import word2vec

 reload(sys)

 sys.setdefaultencoding('utf8')  

 sentences=word2vec.Text8Corpus(u'process_copy_train.csv')

 model=word2vec.Word2Vec(sentences, size=50)

 model[u'美元'.decode("utf-8")]

 y2=model.similarity(u"美元", u"美国")

 print(y2)

 y2=model.similarity(u"美元", u"英镑")

 print(y2)

 y2=model.similarity(u"美元", u"美元")

 print(y2)

 for i in model.most_similar(u"银行"):

     print i[0],i[1]

 str4 = model.most_similar(u"银行".decode("utf-8"))

 print str4

 model.save('/tmp/word2vec_model')

 new_model=gensim.models.Word2Vec.load('/tmp/word2vec_model')

 0.21382438

 0.65352416

 1.0

 商业银行 0.724080383778

 券商 0.67235070467

 同业 0.65898835659

 银行业 0.640146613121

 金融机构 0.628186702728

 中资银行 0.624082624912

 流动性 0.589600920677

 中小银行 0.587715625763

 行 0.576077103615

 信贷 0.575850129128