关于word2vec的原理知识参考文章https://www.cnblogs.com/Micang/p/10235783.html
语料数据来自搜狐新闻2012年6月—7月期间国内，国际，体育，社会，娱乐等18个频道的新闻数据
数据处理参考这篇文章
模型训练：
# -*- coding: utf-8-*- from gensim.models.word2vec import Word2Vec sentences = [['A1','A2'],['A1','A3','A2']] num=0 with open('sohu_train.txt') as trainText: #, encoding='utf-8' for line in trainText: id,catgre,body= line.split('^_^') words=body.replace(' ','').split(' ') sentences.append(words) # if num>1000:break num+=1 # print(sentences) model= Word2Vec(min_count=1) print("start train ...") model.build_vocab(sentences) model.train(sentences,total_examples = model.corpus_count,epochs = model.iter) print("train finished!",num) model.save('./sohu_model/Model') #model.save_word2vec_format('/tmp/mymodel.txt',binary = False) #model.save_word2vec_format('/tmp/mymodel.bin.gz',binary = True) #前一组方法保存的文件不能利用文本编辑器查看但是保存了训练的全部信息，可以在读取后追加训练 #后一组方法保存为word2vec文本格式但是保存时丢失了词汇树等部分信息，不能追加训练 print("save finished!")
模型使用：
# #模型使用 model = Word2Vec.load('./sohu_model/Model') print("load model sesuess!") # model.most_similar(['北京']) print u'most similar with 北京:' for i in model.most_similar("北京"): #计算余弦距离最接近“北京”的10个词 print i[0].decode('utf-8'),i[1] print u'皇帝+女性-男性：' for i in model.most_similar(positive = ['皇帝','女性'],negative = ['男性'],topn = 3):print i[0].decode('utf-8'),i[1] print u'手机+移动-智能：' for i in model.most_similar(positive = ['手机','移动'],negative = ['智能'],topn = 3):print i[0].decode('utf-8'),i[1] print u'电影+科幻-剧情：' for i in model.most_similar(positive = ['电影','科幻'],negative = ['剧情'],topn = 3):print i[0].decode('utf-8'),i[1] print u'北京 vector:' print model['北京']
输出：
C:Python27libsite-packagesgensimutils.py:1212: UserWarning: detected Windows; aliasing chunkize to chunkize_serial warnings.warn("detected Windows; aliasing chunkize to chunkize_serial") load model sesuess! most similar with 北京: C:Python27libsite-packagesgensimmatutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`. if np.issubdtype(vec.dtype, np.int): 南京 0.670382142067 上海 0.661236405373 成都 0.639219224453 杭州 0.63784122467 广州 0.631313323975 深圳 0.624626278877 武汉 0.624594151974 昆明 0.620243370533 长春 0.61394149065 长沙 0.60389906168 皇帝+女性-男性： 哥 0.60431176424 魔术师 0.586149096489 魔女 0.581812143326 手机+移动-智能： 智能手机 0.605030536652 互联网 0.54615008831 苹果 0.539426982403 电影+科幻-剧情： 纪录片 0.648482918739 动画 0.639703273773 迪斯尼 0.61851131916 北京 vector: [-0.08981118 0.18538047 -4.7453156 -1.7730242 2.0390635 2.6085184 5.088326 2.8057106 2.6798103 -1.4660915 2.778077 2.4279277 0.69682086 -3.0003173 2.1341784 0.32419717 -5.2817945 0.18809023 -1.3016417 3.8344557 -0.87402123 -0.26100433 2.8857462 -2.725345 -2.5024219 -0.70686543 -0.4838663 -2.2535524 0.23617841 3.329134 3.9053504 -1.9609474 -3.4581995 1.2530506 -2.079397 1.6266809 0.23296945 1.4600109 -1.9104419 0.80835503 -0.13650164 3.355157 2.4561696 0.6016032 -1.0312346 1.6474588 1.320931 1.4579619 1.8017172 -3.5526018 1.2293625 4.798621 -3.5554793 0.5800354 3.7429204 -0.4906999 -1.3069346 -1.0603447 -0.95469594 -0.35445935 -1.7658769 -3.2370284 -2.2224278 -0.56134427 -0.46095294 2.8492029 2.7202766 -3.3692176 1.1739812 -1.9770668 0.37050596 1.1764477 -0.27834406 5.033905 0.09570877 -0.5670941 -2.1803875 -0.9094422 1.0485793 0.03497482 -2.07145 -0.8045679 -1.8192968 2.6160874 0.5630188 -0.45463613 -0.22750562 2.2233796 3.4276621 -0.8689221 1.5558586 -0.39026013 -1.1843458 -3.378433 -4.2200727 1.6359595 2.27458 -1.6011585 -0.89109504 2.3993087 ]