可以帮我使用python编写基于朴素贝叶斯的中文垃圾邮件分类代码吗

当然可以，以下是一个简单的基于朴素贝叶斯算法的中文垃圾邮件分类代码示例：

import jieba
import os

# 垃圾邮件文件夹路径
spam_path = 'spam/'
# 非垃圾邮件文件夹路径
ham_path = 'ham/'

# 停用词文件路径
stopwords_path = 'stopwords.txt'

# 加载停用词
stopwords = set()
with open(stopwords_path, 'r', encoding='utf-8') as f:
    for line in f:
        stopwords.add(line.strip())

# 获取所有邮件内容及标签
emails = []
for filename in os.listdir(spam_path):
    with open(spam_path + filename, 'r', encoding='utf-8') as f:
        content = f.read()
        emails.append((content, 1))

for filename in os.listdir(ham_path):
    with open(ham_path + filename, 'r', encoding='utf-8') as f:
        content = f.read()
        emails.append((content, 0))

# 分词，并去除停用词
def cut_words(content):
    words = jieba.cut(content)
    return [word for word in words if word not in stopwords]

# 统计词频
def count_words(words):
    word_count = {}
    for word in words:
        word_count[word] = word_count.get(word, 0) + 1
    return word_count

# 计算每个词在垃圾邮件和非垃圾邮件中的出现次数
spam_word_count = {}
ham_word_count = {}
spam_total_count = 0
ham_total_count = 0
for email in emails:
    words = cut_words(email[0])
    if email[1] == 1:
        spam_total_count += 1
        for word in words:
            spam_word_count[word] = spam_word_count.get(word, 0) + 1
    else:
        ham_total_count += 1
        for word in words:
            ham_word_count[word] = ham_word_count.get(word, 0) + 1

# 计算每个词在垃圾邮件和非垃圾邮件中的出现概率
p_word_spam = {}
p_word_ham = {}
for word in set(list(spam_word_count.keys()) + list(ham_word_count.keys())):
    p_word_spam[word] = (spam_word_count.get(word, 0) + 1) / (spam_total_count + 2)
    p_word_ham[word] = (ham_word_count.get(word, 0) + 1) / (ham_total_count + 2)

# 定义分类函数
def classify(content):
    words = cut_words(content)
    p_spam = 1
    p_ham = 1
    for word in words:
        p_spam *= p_word_spam.get(word, 1)
        p_ham *= p_word_ham.get(word, 1)
    if p_spam > p_ham:
        return '垃圾邮件'
    else:
        return '非垃圾邮件'

# 测试分类函数
with open('test.txt', 'r', encoding='utf-8') as f:
    content = f.read()
    classification = classify(content)
    print(classification)

在代码中，我们首先加载停用词，并使用jieba分词对邮件内容进行处理。然后使用朴素贝叶斯算法统计每个词在垃圾邮件和非垃圾邮件中的出现次数和出现概率。最后定义了一个分类函数，用于对新邮件进行分类。运行测试代码可以得到分类结果。当然，为了获得更好的分类效果，你可能需要更多的训练数据和调整模型参数。