# -- coding utf-8 --import mathimport numpy as npimport jiebaimport jiebaposseg as psgfrom gensim import corpora modelsfrom jieba import analyseimport functools# 步骤2定义停用词表加载函数。停用词表加载方法def get_stopword

根据你提供的代码，我注意到在tfidf_extract和textrank_extract函数中，关键词的提取结果是直接打印在控制台上的。如果你希望将结果保存到文件中，你可以将关键词保存到一个字符串中，然后再将该字符串写入文件中。下面是修改后的代码示例：

def tfidf_extract(word_list, pos=False, keyword_num=10):
    doc_list = load_data(pos)
    idf_dic, default_idf = train_idf(doc_list)
    tfidf_model = TfIdf(idf_dic, default_idf, word_list, keyword_num)
    keywords = tfidf_model.get_tfidf()

    # 将关键词保存到字符串中
    result = ""
    for keyword in keywords:
        result += keyword + "/"

    # 将关键词写入文件
    with open("d:/Users/Administrator/Desktop/data/result.txt", "w", encoding='utf-8') as f:
        f.write('TF-IDF模型结果:\n')
        f.write(result)


def textrank_extract(text, pos=False, keyword_num=10):
    textrank = analyse.textrank
    keywords = textrank(text, keyword_num)

    # 将关键词保存到字符串中
    result = ""
    for keyword in keywords:
        result += keyword + "/"

    # 将关键词写入文件
    with open("d:/Users/Administrator/Desktop/data/result.txt", "a", encoding='utf-8') as f:
        f.write('\nTextRank模型结果:\n')
        f.write(result)


# 调用函数进行关键词提取并保存到文件
with open("d:/Users/Administrator/Desktop/data/corpus.txt", "r", encoding='utf-8') as f:
    text = f.read()  # 读取文本

seg_list = seg_to_list(text, pos)
filter_list = word_filter(seg_list, pos)

tfidf_extract(filter_list, True, 10)
textrank_extract(text, True, 10)

这样，关键词提取结果就会保存到d:/Users/Administrator/Desktop/data/result.txt文件中了。