参考资料 ：

接上篇 ：

import jiebaall_list = jieba.cut(xl['工作内容'][0:6],cut_all=True)print(all_list)every_one = xl['工作内容'].apply(lambda x:jieba.cut(x))import tracebackdef filtered_punctuations(token_list):    try:        punctuations = [' ', '\n', '\t', ',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%','：',                        '/','\xa0','。','；','、']        token_list_without_punctuations = [word for word in token_list                                                         if word not in punctuations]        #print "[INFO]: filtered_punctuations is finished!"        return token_list_without_punctuations    except Exception as e:        print (traceback.print_exc())from gensim.models import Doc2Vec,Word2Vecimport gensimdef list_crea(everyone):    list_word = []    for k in everyone:        fenci= filtered_punctuations(k)        list_word.append(fenci)    return list_wordaa_word = list_crea(every_one)print(type(aa_word))  #aa_word 是 个 嵌套的list   [[1,2,3], [4,5,6], [7,8,9]]model = Word2Vec(aa_word, min_count=1)    # 训练模型，参考英文官网，在上面say_vector = model['java']  # get vector for wordmodel.similarity('计算', '计算机')