python中的jieba实现对一个文件夹中的所有文档进行分词，根据停用词表去除停用词，基于文档中的非停用词及其TF-IDF值，生成该文档的词向量，具体代码...

好的，以下是代码实现：

import os
import jieba
import math

# 读取停用词，返回一个列表
def get_stopwords(file_path):
    stopwords = []
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            stopwords.append(line.strip())
    return stopwords

# 计算TF-IDF值
def get_tf_idf(word, words_list, words_count, docs_count):
    tf = words_list.count(word) / words_count
    idf = math.log(docs_count / (sum([1 for w_l in words_list if word in w_l])))
    return tf * idf

# 对一个文档进行分词，返回词语列表
def cut_words(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        text = f.read()
        words_list = jieba.lcut(text)
    return words_list

# 对一个文档生成词向量
def get_doc_vector(words_list, stopwords, words_count, docs_count):
    vector = []
    for word in words_list:
        if word not in stopwords:
            tf_idf = get_tf_idf(word, words_list, words_count, docs_count)
            vector.append((word, tf_idf))
    return vector

# 遍历文件夹，生成所有文档的词向量
def get_all_vectors(folder_path, stopwords_path):
    stopwords = get_stopwords(stopwords_path)
    all_vectors = []
    docs_count = 0
    for root, dirs, files in os.walk(folder_path):
        for file in files:
            file_path = os.path.join(root, file)
            words_list = cut_words(file_path)
            words_count = len(words_list)
            vector = get_doc_vector(words_list, stopwords, words_count, docs_count)
            all_vectors.append(vector)
            docs_count += 1
    return all_vectors

使用方法：

folder_path = 'your_folder_path'
stopwords_path = 'your_stopwords_path'
all_vectors = get_all_vectors(folder_path, stopwords_path)

其中，`folder_path` 为待分词的文件夹路径，`stopwords_path` 为停用词表路径。`all_vectors` 为生成的所有文档的词向量列表，每个文档的词向量为一个二元组的列表，形如：`[('词1', tfidf1), ('词2', tfidf2), ...]`。