1.jieba.analyse.extract_tags(text)  text必须是一连串的字符串才可以

第一步：进行语料库的读取

第二步：进行分词操作

第三步：载入停用词，同时对分词后的语料库进行停用词的去除

第四步：选取一段文本分词列表，串接成字符串，使用jieba.analyse.extract_tags提取主题词

import pandas as pdimport numpy as npimport jieba# 1.导入数据语料的新闻数据df_data = pd.read_table('data/val.txt', names=['category', 'theme', 'URL', 'content'], encoding='utf-8')# 2.对语料库进行分词操作df_contents = df_data.content.values.tolist()# list of list 结构Jie_content = []for df_content in df_contents:    split_content = jieba.lcut(df_content)    if len(split_content) > 1 and split_content != '\t\n':        Jie_content.append(split_content)# 3. 导入停止词的语料库, sep='\t'表示分隔符， quoting控制引号的常量， names=列名， index_col=False，不用第一列做为行的列名， encodingstopwords = pd.read_csv('stopwords.txt', sep='\t', quoting=3, names=['stopwords'], index_col=False, encoding='utf-8')print(stopwords.head())# 对文本进行停止词的去除def drop_stops(Jie_content, stopwords):    clean_content = []    all_words = []    for j_content in Jie_content:        line_clean = []        for line in j_content:            if line in stopwords:                continue            line_clean.append(line)            all_words.append(line)        clean_content.append(line_clean)    return clean_content, all_words# 将DateFrame的stopwords数据转换为list形式stopwords = stopwords.stopwords.values.tolist()clean_content, all_words = drop_stops(Jie_content, stopwords)print(clean_content[0])#4.  使用jieba分词器，提取文本的关键字import jieba.analyseindex = 2000content_word = ''.join(clean_content[index])content_text = ' '.join(jieba.analyse.extract_tags(content_word, topK=5, withWeight=False))print(content_word)print(content_text)