gensim训练模型并使用以人民的名义小说为例子

首先对小说进行预处理，使用jieba分词进行分词

import jieba
import jieba.analyse

jieba.suggest_freq('沙瑞金', True)
jieba.suggest_freq('田国富', True)
jieba.suggest_freq('高育良', True)
jieba.suggest_freq('侯亮平', True)
jieba.suggest_freq('钟小艾', True)
jieba.suggest_freq('陈岩石', True)
jieba.suggest_freq('欧阳菁', True)
jieba.suggest_freq('易学习', True)
jieba.suggest_freq('王大路', True)
jieba.suggest_freq('蔡成功', True)
jieba.suggest_freq('孙连城', True)
jieba.suggest_freq('季昌明', True)
jieba.suggest_freq('丁义珍', True)
jieba.suggest_freq('郑西坡', True)
jieba.suggest_freq('赵东来', True)
jieba.suggest_freq('高小琴', True)
jieba.suggest_freq('赵瑞龙', True)
jieba.suggest_freq('林华华', True)
jieba.suggest_freq('陆亦可', True)
jieba.suggest_freq('刘新建', True)
jieba.suggest_freq('刘庆祝', True)

with open('./in_the_name_of_people.txt', encoding="utf8") as f:
    document = f.read()
    # print(document)
    # document_decode = document.decode('GBK')

    document_cut = jieba.cut(document)
    # print  ' '.join(jieba_cut)  //如果打印结果，则分词效果消失，后面的result无法显示
    result = ' '.join(document_cut)
    # result = result.encode('utf-8')
    # print(result)
    with open('./in_the_name_of_people_segment.txt', 'w', encoding="utf8") as f2:
        f2.write(result)

训练模型：

import logging
import os
from gensim.models import word2vec

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

sentences = word2vec.LineSentence('./in_the_name_of_people_segment.txt')

model = word2vec.Word2Vec(sentences, hs=1, min_count=1, window=3)

模型的三种应用：

# 找出相近词的集合
print(model.wv.similar_by_key("沙瑞金", topn=5))

# 查看两个词的相近程度
print("沙瑞金|高育良的相似度：", model.wv.similarity("沙瑞金", "高育良"))

# 找出不同类的词
print("沙瑞金 高育良 李达康 刘庆祝中不同类别的词为：",model.wv.doesnt_match(u"沙瑞金 高育良 李达康 刘庆祝".split()))