0，如果您觉得操作麻烦，可以直接直接下载生成好的wiki.zh.text.model模型

     https://download.csdn.net/download/luolinll1212/10640451

1，下载中文维基百科 https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2,并安装gensim

      pip install gensim

2，创建process_wiki.py，代码如下

import logging
import os.path
import sys

from gensim.corpora import WikiCorpus

if __name__ == '__main__':
    program = os.path.basename(sys.argv[0])
    logger = logging.getLogger(program)

    logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
    logging.root.setLevel(level=logging.INFO)
    logger.info("running %s" % ' '.join(sys.argv))

    # check and process input arguments
    if len(sys.argv) < 3:
         print(globals()['__doc__'] % locals())
         sys.exit(1)
    inp, outp = sys.argv[1:3]
    space = " "
    i = 0

    output = open(outp, 'w',encoding="utf-8")   # 本人采用win7环境，所以要采用utf-8模式
    # output = open(outp, 'w')
    wiki = WikiCorpus(inp, lemmatize=False, dictionary={})
    for text in wiki.get_texts():
        output.write(space.join(text) + "\n")
        i = i + 1
        if (i % 10000 == 0):
            logger.info("Saved " + str(i) + " articles")

    output.close()
    logger.info("Finished Saved " + str(i) + " articles")

执行：python process_wiki.py zhwiki-latest-pages-articles.xml.bz2 wiki.zh.text。此处参数为4个。

3，创建train_word2vec_model.py，代码如下

import logging
import multiprocessing
import os.path
import sys

from gensim.models import Word2Vec
from gensim.models.word2vec import PathLineSentences

if __name__ == '__main__':
    program = os.path.basename(sys.argv[0])
    logger = logging.getLogger(program)
    logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
    logging.root.setLevel(level=logging.INFO)
    logger.info("running %s" % ' '.join(sys.argv))
    check and process input arguments
    if len(sys.argv) < 4:
        print(globals()['__doc__'] % locals())
        sys.exit(1)
    input_dir, outp1, outp2 = sys.argv[1:4]

    model = Word2Vec(PathLineSentences(input_dir),
                     size=256, window=10, min_count=5,
                     workers=multiprocessing.cpu_count(), iter=10)
    model.save(outp1)
    model.wv.save_word2vec_format(outp2, binary=False)

执行：python train_word2vec_model.py wiki.zh.text.jian.seg.utf-8 wiki.zh.text.model wiki.zh.text.vector。此处参数为5个。

4，按照以上步骤执行完，生成4个文件

5，将此4个文件放入model文件夹，运行