diff --git a/danmu.py b/danmu.py
index af373c2..c6cc24c 100644
--- a/danmu.py
+++ b/danmu.py
@@ -1,120 +1,124 @@
-import re
-import requests
-import json
-from wordcloud import WordCloud
-from collections import Counter
-import pandas as pd
-import jieba
-
-# 用于获得请求的头，应对B站的反爬虫机制
-headers = {
-    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 Edg/128.0.0.0",
-    "Referer": "https://www.bilibili.com/video/BV1ji421r7PB/?spm_id_from=333.337.search-card.all.click&vd_source=6d043574d81a3f216c41cc281767c59e",
-    "Cookie": "buvid4=F793982E-B1BE-6615-D504-4A3BF6DC079274572-022081216-zrNi2ZH69Dhc8fOw09IIaA%3D%3D; buvid_fp_plain=undefined; DedeUserID=254629681; DedeUserID__ckMd5=23182279794f6f43; header_theme_version=CLOSE; enable_web_push=DISABLE; rpdid=0zbfVHb84m|bujW4qg1|H3r|3w1RkXNz; FEED_LIVE_VERSION=V_WATCHLATER_PIP_WINDOW3; hit-dyn-v2=1; CURRENT_FNVAL=4048; LIVE_BUVID=AUTO3317219069887705; buvid3=575DAC67-0FE1-B3C3-8A9F-32190F231A1F24035infoc; b_nut=1723377925; _uuid=24521A4B-895E-10E6A-2555-A9EF812A1AC136123infoc; fingerprint=45b7e451fad392b858fc6e95bbb4231d; PVID=1; SESSDATA=7bb24682%2C1741874111%2Cf9490%2A92CjB3NDHhJCIr34LBlg2TTS7vPGQ72Kh3s3DVcaEn0JkQ_hXc7q6MgsK9Wjb-Z6NTf68SVnlnTkJQZmtmRXMxZnc1RDZpYUFnS1BlTmptUklUSkcyQUNxaWg4Smpla1VCZHdkLVc4dW9OWU4zZm1PaVhuMzFtcnNvQ2JMQzFiTFVleC1HbXc0Sm53IIEC; bili_jct=895f9e091815ee492e57345dc62b8757; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjY1ODEzMjMsImlhdCI6MTcyNjMyMjA2MywicGx0IjotMX0.47Js9n97GQecalPWdc-MNrDVUOz2-FfY_DlB72DUlZM; bili_ticket_expires=1726581263; CURRENT_QUALITY=32; bp_t_offset_254629681=977402825627664384; buvid_fp=45b7e451fad392b858fc6e95bbb4231d; home_feed_column=5; browser_resolution=1528-704; b_lsid=18C6BCCB_191F83A85C1"
-}
-
-def get_bvlist():
-    # 获取BV号
-    bvlist = []
-    for page in range(1, 11):
-        # 搜索“2024巴黎奥运会”的URL
-        url = f'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page={page}&page_size=30&pubtime_begin_s=0&pubtime_end_s=0&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&#39'
-        # 发送请求
-        response = requests.get(url, headers=headers)
-        response.encoding='urf-8'
-        # 使用正则表达式得到BV号
-        tem_bvlist = re.findall('"bvid":"(.*?)"', response.text)
-        bvlist.extend(list(set(tem_bvlist)))
-    print("BV号获取完成")
-    return bvlist
-
-def get_cidlist(bvlist):
-    # 传入获取到的BV号，获取对应的cid
-    cidlist=[]
-    for bvid in bvlist:
-        # 由于获取cid的URL
-        url = f'https://api.bilibili.com/x/player/pagelist?bvid={bvid}&jsonp=jsonp'
-        # 发送请求
-        res = requests.get(url,headers=headers)
-        res.encoding = 'urf-8'
-        # 利用JSON提取cid
-        json_dict = json.loads(res.text)
-        cidlist.append(json_dict["data"][0]["cid"])
-    print("cid获取完成")
-    return cidlist
-
-def get_danmu(cidlist):
-    # 利用传入的cid，获取对应的弹幕
-    count = 1
-    for cid in cidlist:
-        # 弹幕所在的URL
-        url = f'http://comment.bilibili.com/{cid}.xml'
-        # 发送请求
-        response = requests.get(url, headers=headers)
-        response.encoding = 'utf-8'
-        # 利用正则表达式获取弹幕
-        content_list = re.findall('<d p=".*?">(.*?)</d>', response.text)
-        for danmu in content_list:
-            with open('弹幕.txt', 'a', encoding='utf-8') as f:
-                f.write(danmu + '\n')
-        print(f'----------第{count}个视频弹幕获取完毕！----------')
-        count +=1
-
-def get_ai_related_danmu():
-    # 统计与AI技术应用相关的弹幕
-    ai_related_danmu = []
-    # AI相关的关键词列表
-    ai_keywords = {'ai', 'ai生成', 'ai合成', 'ai配音', 'gpt', '人工智能', '机器学习', '深度学习', '神经网络',
-                   '自然语言处理', '计算机视觉', '语音识别', '人脸识别', '无人驾驶'}
-    # 读取弹幕文件
-    with open('弹幕.txt', 'r', encoding='utf-8') as file:
-        danmu_lines = file.readlines()
-    # 去除原本的换行符和空格，方便统计
-    danmu_cleaned = [line.strip() for line in danmu_lines]
-    # 统计与AI技术应用相关的弹幕
-    for danmu in danmu_cleaned:
-        if any(keyword in danmu.lower() for keyword in ai_keywords):
-            # 对含有“ai”关键字但内容并不是AI技术应用相关的弹幕需另外判断
-            if 'ai' in danmu.lower() and re.search(r'(?<![\u4e00-\u9fa5])[aiAI](?![\u4e00-\u9fa5])', danmu):
-                continue
-            ai_related_danmu.append(danmu)
-
-    # 统计每种弹幕的数量
-    danmu_count = Counter(ai_related_danmu)
-
-    # 获取数量排名前8的弹幕
-    top_8_danmu = danmu_count.most_common(8)
-
-    # 创建一个DataFrame来存储数据
-    df = pd.DataFrame(top_8_danmu, columns=['弹幕内容', '数量'])
-
-    # 将DataFrame写入Excel文件
-    df.to_excel('AI弹幕统计.xlsx', index=False)
-    return(ai_related_danmu)
-
-def get_wordcloud(ai_danmu):
-    # 首先，将弹幕列表中的每个元素连接成一个长字符串，元素之间用空格分隔
-    ai = ' '.join(ai_danmu)
-    # 使用jieba分词对连接后的字符串进行分词，并将分词结果再次连接成一个长字符串，分词之间用空格分隔
-    cut_t = ' '.join(jieba.cut(ai))
-    # 创建一个WordCloud对象，设置词云图的参数
-    wordcloud = WordCloud(background_color="white",     # 设置背景颜色为白色
-                          width=800,                    # 设置词云图的宽度
-                          height=600,                   # 设置词云图的高度
-                          max_words=150,                # 设置词云图中显示的最大单词数
-                          max_font_size=80,             # 设置词云图中最大字号
-                          font_path='msyh.ttc',         # 设置字体路径，确保能够显示中文
-                          contour_width=2,              # 设置词云图的轮廓宽度
-                          contour_color='steelblue',    # 设置词云图轮廓的颜色
-                          repeat='true'                 # 设置单词课重复出现
-                          ).generate(cut_t)
-    # 将生成的词云图保存为PNG文件
-    wordcloud.to_file('词云图.png')
-
-
-if __name__ == '__main__':
-    bvlist = get_bvlist()
-    cidlist = get_cidlist(bvlist)
-    get_danmu(cidlist)
-    ai_danmu = get_ai_related_danmu()
+import re
+import requests
+import json
+from wordcloud import WordCloud
+from collections import Counter
+import pandas as pd
+import jieba
+
+# 用于获得请求的头，应对B站的反爬虫机制
+headers = {
+    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 Edg/128.0.0.0",
+    "Referer": "https://www.bilibili.com/video/BV1ji421r7PB/?spm_id_from=333.337.search-card.all.click&vd_source=6d043574d81a3f216c41cc281767c59e",
+    "Cookie": "buvid4=F793982E-B1BE-6615-D504-4A3BF6DC079274572-022081216-zrNi2ZH69Dhc8fOw09IIaA%3D%3D; buvid_fp_plain=undefined; DedeUserID=254629681; DedeUserID__ckMd5=23182279794f6f43; header_theme_version=CLOSE; enable_web_push=DISABLE; rpdid=0zbfVHb84m|bujW4qg1|H3r|3w1RkXNz; FEED_LIVE_VERSION=V_WATCHLATER_PIP_WINDOW3; hit-dyn-v2=1; CURRENT_FNVAL=4048; LIVE_BUVID=AUTO3317219069887705; buvid3=575DAC67-0FE1-B3C3-8A9F-32190F231A1F24035infoc; b_nut=1723377925; _uuid=24521A4B-895E-10E6A-2555-A9EF812A1AC136123infoc; fingerprint=45b7e451fad392b858fc6e95bbb4231d; PVID=1; SESSDATA=7bb24682%2C1741874111%2Cf9490%2A92CjB3NDHhJCIr34LBlg2TTS7vPGQ72Kh3s3DVcaEn0JkQ_hXc7q6MgsK9Wjb-Z6NTf68SVnlnTkJQZmtmRXMxZnc1RDZpYUFnS1BlTmptUklUSkcyQUNxaWg4Smpla1VCZHdkLVc4dW9OWU4zZm1PaVhuMzFtcnNvQ2JMQzFiTFVleC1HbXc0Sm53IIEC; bili_jct=895f9e091815ee492e57345dc62b8757; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjY1ODEzMjMsImlhdCI6MTcyNjMyMjA2MywicGx0IjotMX0.47Js9n97GQecalPWdc-MNrDVUOz2-FfY_DlB72DUlZM; bili_ticket_expires=1726581263; CURRENT_QUALITY=32; bp_t_offset_254629681=977402825627664384; buvid_fp=45b7e451fad392b858fc6e95bbb4231d; home_feed_column=5; browser_resolution=1528-704; b_lsid=18C6BCCB_191F83A85C1"
+}
+
+def get_bvlist():
+    # 获取BV号
+    bvlist = []
+    for page in range(1, 11):
+        # 搜索“2024巴黎奥运会”的URL
+        url = f'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page={page}&page_size=30&pubtime_begin_s=0&pubtime_end_s=0&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&#39'
+        # 发送请求
+        response = requests.get(url, headers=headers)
+        response.encoding='urf-8'
+        # 使用正则表达式得到BV号
+        tem_bvlist = re.findall('"bvid":"(.*?)"', response.text)
+        bvlist.extend(list(set(tem_bvlist)))
+    print("BV号获取完成")
+    return bvlist
+
+def get_cidlist(bvlist):
+    # 传入获取到的BV号，获取对应的cid
+    cidlist=[]
+    for bvid in bvlist:
+        # 由于获取cid的URL
+        url = f'https://api.bilibili.com/x/player/pagelist?bvid={bvid}&jsonp=jsonp'
+        # 发送请求
+        res = requests.get(url,headers=headers)
+        res.encoding = 'urf-8'
+        # 利用JSON提取cid
+        json_dict = json.loads(res.text)
+        cidlist.append(json_dict["data"][0]["cid"])
+    print("cid获取完成")
+    return cidlist
+
+def get_danmu(cidlist):
+    # 利用传入的cid，获取对应的弹幕
+    count = 1
+    for cid in cidlist:
+        # 弹幕所在的URL
+        url = f'http://comment.bilibili.com/{cid}.xml'
+        # 发送请求
+        response = requests.get(url, headers=headers)
+        response.encoding = 'utf-8'
+        # 利用正则表达式获取弹幕
+        content_list = re.findall('<d p=".*?">(.*?)</d>', response.text)
+        for danmu in content_list:
+            with open('弹幕.txt', 'a', encoding='utf-8') as f:
+                f.write(danmu + '\n')
+        print(f'----------第{count}个视频弹幕获取完毕！----------')
+        count +=1
+
+def get_ai_related_danmu():
+    # 统计与AI技术应用相关的弹幕
+    ai_related_danmu = []
+    # AI相关的关键词列表
+    ai_keywords = {'ai', 'ai生成', 'ai合成', 'ai配音', 'gpt', '人工智能', '机器学习', '深度学习', '神经网络',
+                   '自然语言处理', '计算机视觉', '语音识别', '人脸识别', '无人驾驶'}
+    # 读取弹幕文件
+    with open('弹幕.txt', 'r', encoding='utf-8') as file:
+        danmu_lines = file.readlines()
+    # 去除原本的换行符和空格，方便统计
+    danmu_cleaned = [line.strip() for line in danmu_lines]
+    # 统计与AI技术应用相关的弹幕
+    for danmu in danmu_cleaned:
+        if any(keyword in danmu.lower() for keyword in ai_keywords):
+            # 对含有“ai”关键字但内容并不是AI技术应用相关的弹幕需另外判断
+            if 'ai' in danmu.lower() and re.search(r'(?<![\u4e00-\u9fa5])[aiAI](?![\u4e00-\u9fa5])', danmu):
+                continue
+            ai_related_danmu.append(danmu)
+
+    # 统计每种弹幕的数量
+    danmu_count = Counter(ai_related_danmu)
+
+    # 获取数量排名前8的弹幕
+    top_8_danmu = danmu_count.most_common(8)
+
+    # 创建一个DataFrame来存储数据
+    df = pd.DataFrame(top_8_danmu, columns=['弹幕内容', '数量'])
+
+    # 将DataFrame写入Excel文件
+    df.to_excel('AI弹幕统计.xlsx', index=False)
+    return(ai_related_danmu)
+
+def get_wordcloud(ai_danmu):
+    # 首先，将弹幕列表中的每个元素连接成一个长字符串，元素之间用空格分隔
+    ai = ' '.join(ai_danmu)
+    # 使用jieba分词对连接后的字符串进行分词，并将分词结果再次连接成一个长字符串，分词之间用空格分隔
+    cut_t = ' '.join(jieba.cut(ai))
+    # 设置停用词，减少无效信息
+    stop_words = set()
+    content = [line.strip() for line in open('停用词.txt', 'r', encoding='utf-8').readlines()]
+    stop_words.update(content)
+    # 创建一个WordCloud对象，设置词云图的参数
+    wordcloud = WordCloud(background_color="white",     # 设置背景颜色为白色
+                          width=800,                    # 设置词云图的宽度
+                          height=600,                   # 设置词云图的高度
+                          max_words=150,                # 设置词云图中显示的最大单词数
+                          max_font_size=80,             # 设置词云图中最大字号
+                          font_path='msyh.ttc',         # 设置字体路径，确保能够显示中文
+                          contour_width=2,              # 设置词云图的轮廓宽度
+                          contour_color='steelblue',    # 设置词云图轮廓的颜色
+                          repeat='true',                # 设置单词课重复出现
+                          stopwords = stop_words,       # 设置停用词
+                          ).generate(cut_t)
+    # 将生成的词云图保存为PNG文件
+    wordcloud.to_file('词云图.png')
+
+if __name__ == '__main__':
+    bvlist = get_bvlist()
+    cidlist = get_cidlist(bvlist)
+    get_danmu(cidlist)
+    ai_danmu = get_ai_related_danmu()
     get_wordcloud(ai_danmu)
\ No newline at end of file