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，以下是实现的具体代码：

import os
import jieba
import math

# 加载停用词表
stopwords = []
with open('stopwords.txt', 'r', encoding='utf-8') as f:
    for line in f.readlines():
        stopwords.append(line.strip())

# 计算TF-IDF值
def calculate_tf_idf(word, doc_freq, total_docs):
    tf = doc_freq / total_docs
    idf = math.log(total_docs / (doc_freq + 1))
    return tf * idf

# 对一个文档进行分词
def segment_doc(doc_path):
    with open(doc_path, 'r', encoding='utf-8') as f:
        content = f.read()
        words = jieba.cut(content)
        words = [word for word in words if word not in stopwords]
        return words

# 对一个文档生成词向量
def generate_word_vector(doc_path, words_dict, total_docs):
    words = segment_doc(doc_path)
    word_vector = {}
    for word in words:
        if word not in words_dict:
            continue
        if word not in word_vector:
            word_vector[word] = 0
        word_vector[word] += 1
    
    for word, freq in word_vector.items():
        doc_freq = words_dict[word]
        tf_idf_value = calculate_tf_idf(word, doc_freq, total_docs)
        word_vector[word] = tf_idf_value
        
    return word_vector

# 获取所有文档分词后的词频
def get_words_dict(docs_dir):
    words_dict = {}
    total_docs = 0
    for filename in os.listdir(docs_dir):
        doc_path = os.path.join(docs_dir, filename)
        words = segment_doc(doc_path)
        for word in words:
            if word not in words_dict:
                words_dict[word] = 0
            words_dict[word] += 1
        total_docs += 1
    return words_dict, total_docs

# 对整个文档集生成词向量矩阵
def generate_word_vectors_matrix(docs_dir):
    words_dict, total_docs = get_words_dict(docs_dir)
    word_vectors_matrix = []
    for filename in os.listdir(docs_dir):
        doc_path = os.path.join(docs_dir, filename)
        word_vector = generate_word_vector(doc_path, words_dict, total_docs)
        word_vectors_matrix.append(word_vector)
    return word_vectors_matrix

其中，`docs_dir`是文件夹路径，表示需要对该文件夹中的所有文档进行分词和词向量的生成。停用词表文件为`stopwords.txt`，需要提前准备好。生成的词向量矩阵是一个二维列表，每一行表示一个文档的词向量。