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ptlqmxye5 2 months ago
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bilibili_search.py
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import requests
import json
from bs4 import BeautifulSoup
from collections import Counter
import pandas as pd
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import time
import jieba
# B站搜索API URL
search_url = 'https://api.bilibili.com/x/web-interface/wbi/search/type'
# B站视频详情API URL，用于获取视频的cid
video_info_url = 'https://api.bilibili.com/x/web-interface/view'
# B站弹幕API URL
danmu_url = 'https://api.bilibili.com/x/v1/dm/list.so'
def search_bilibili(query, total_results):
num_per_page = 42 # 每页最大视频数
pages_needed = (total_results + num_per_page - 1) // num_per_page # 计算需要多少页
video_list = []
for page in range(1, pages_needed + 1):
params = {
'__refresh__': 'true',
'_extra': '',
'context': '',
'page_size': num_per_page,
'from_source': '',
'from_spmid': '333.337',
'platform': 'pc',
'highlight': '1',
'single_column': '0',
'keyword': query,
'qv_id': '0EnOHi82F62j2usODhMghThN7EvXEZmj',
'source_tag': '3',
'dynamic_offset': 30,
'search_type': 'video',
'w_rid': '16f27d62ff40f1a5f935a6af26432c81',
'wts': '1726306000',
'page': page # 设置页码
}
headers = {
'accept': 'application/json, text/plain, */*',
'accept-encoding': 'gzip, deflate, br, zstd',
'accept-language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7,en-GB;q=0.6',
'cookie': 'DedeUserID=1075156086; DedeUserID__ckMd5=7460241d769e1da4; buvid4=9980B4C0-302E-C6A9-122A-0EFE06E4B5F435899-022102715-X83v1qigvaWQdhtSeo%2BvYQ%3D%3D; enable_web_push=DISABLE; buvid3=0DD4B4A8-5B28-59F0-F5EB-9EB31F483AF226299infoc; b_nut=1699086426; _uuid=1FCED779-E59E-F3CA-81A8-817C10CCF3105C25422infoc; header_theme_version=CLOSE; PVID=1; buvid_fp=395bc05f8612d5e47df093ecc1b2bd8e; rpdid=|(J|)Y)JlmJJ0J\'u~|~m|lJ|Y; CURRENT_FNVAL=4048; CURRENT_QUALITY=80; FEED_LIVE_VERSION=V_HEADER_LIVE_NO_POP; SESSDATA=45375f4b%2C1741793139%2Ccfd7b%2A92CjCDJGeSRO5decbO4E62OzjFfKGaAkW2xZVJMSoyMiINCOFoCUntmY4_rMzO8gFMJzYSVkNDTFpKa0h6TWEyS3NNMm5oUkluSnc2OGsySll6MUVBWGFJbmdmZ1VST3pWOWFiek92cnk4S3BhZVRpNFgtLWxacE5iZ3NlX29DdGx3dUJLZWJja253IIEC; bili_jct=f86ff49e42fb369eb9cfa114fa804019; sid=4qsj12c6; home_feed_column=5; browser_resolution=1528-738; bsource=search_bing; bp_t_offset_1075156086=976968501354823680; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjY3MTY5MzMsImlhdCI6MTcyNjQ1NzY3MywicGx0IjotMX0.7WQjSxEb__Z8q6mXZZVKcYfGj_p_EP-8VkK9httVQQA; bili_ticket_expires=1726716873; b_lsid=8197BB1F_191FAD926B7',
'origin': 'https://search.bilibili.com',
'referer': 'https://search.bilibili.com/all',
'sec-ch-ua': '"Chromium";v="128", "Not;A=Brand";v="24", "Microsoft Edge";v="128"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-site',
'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'
}
response = requests.get(search_url, params=params, headers=headers)
print(f"Page {page} HTTP Status Code: {response.status_code}")
if response.status_code == 412:
print("请求被阻止，等待1秒重试...")
time.sleep(1)
continue
try:
data = response.json()
print(f"Page {page} Parsed JSON Data:")
print(data)
except json.JSONDecodeError:
print(f"Page {page} 无法解析 JSON 数据")
continue
if data['code'] != 0:
print(f"Page {page} Failed to fetch data from Bilibili API")
continue
videos = data['data']['result']
for video in videos:
video_id = video['bvid']
video_list.append(video_id)
if len(video_list) >= total_results:
break
if len(video_list) >= total_results:
break
return video_list
def get_video_cid(bvid):
# 请求视频的详情信息，获取cid
params = {
'bvid': bvid
}
headers = {
'accept': 'application/json, text/plain, */*',
'accept-encoding': 'gzip',
'accept-language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7,en-GB;q=0.6',
'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'
}
response = requests.get(video_info_url, params=params, headers=headers)
print(f"Video Info HTTP Status Code: {response.status_code}")
if response.status_code != 200:
print(f"Failed to fetch video info for {bvid}")
return None
try:
data = response.json()
if 'cid' in data['data']:
return data['data']['cid']
else:
print(f"CID not found for video {bvid}")
return None
except json.JSONDecodeError:
print("无法解析视频信息的 JSON 数据")
return None
def fetch_danmu(cid):
params = {
'oid': cid
}
headers = {
'accept': 'application/xml, text/xml, */*',
'accept-encoding': 'gzip',
'accept-language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7,en-GB;q=0.6',
'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'
}
response = requests.get(danmu_url, params=params, headers=headers)
print(f"Danmu HTTP Status Code: {response.status_code}")
if response.status_code != 200:
print(f"Failed to fetch danmu for CID {cid}")
return []
content = response.content.decode('utf-8')
print("Danmu Response Content:")
print(content)
soup = BeautifulSoup(content, 'xml')
danmu_texts = [d.text for d in soup.find_all('d')]
return danmu_texts
def count_and_rank_danmu(danmu_texts):
ai_keywords = ['人工智能', '机器学习', '深度学习', '自然语言处理', '计算机视觉', '智能算法', '大数据', 'AI', '智能制造', '智能家居', '智能医疗', '物联网', '云计算', '智能服务', '自动化']
top_n = 8
# 统计每种弹幕的频率
counter = Counter(danmu_texts)
# 统计与 AI 技术应用相关的弹幕频率
ai_counter = Counter()
keyword_counter = Counter()
for text, count in counter.items():
# 统计 AI 关键词的出现次数
for keyword in ai_keywords:
if keyword in text:
ai_counter[text] += count
keyword_counter[keyword] += count
# 排名前 top_n 的弹幕
ranked_ai_danmu = ai_counter.most_common(top_n)
# 输出每种 AI 关键词的出现次数
print("AI 技术应用关键词的出现次数:")
for keyword, count in keyword_counter.items():
print(f"{keyword}: {count} 次")
# 输出排名前 top_n 的弹幕及其频率
print(f"\n排名前 {top_n} 的 AI 技术应用弹幕:")
for text, count in ranked_ai_danmu:
print(f"弹幕: {text} - 频率: {count} 次")
# 将统计结果导出到 Excel
export_to_excel(ranked_ai_danmu, keyword_counter)
def export_to_excel(ranked_ai_danmu, keyword_counter):
# 创建 DataFrame，不进行分词，保持原始弹幕
df_danmu = pd.DataFrame(ranked_ai_danmu, columns=['弹幕', '频率'])
df_keywords = pd.DataFrame(keyword_counter.items(), columns=['关键词', '出现次数'])
# 保存到 Excel 文件
with pd.ExcelWriter('danmu_statistics.xlsx') as writer:
df_danmu.to_excel(writer, sheet_name='AI 技术应用弹幕', index=False)
df_keywords.to_excel(writer, sheet_name='AI 技术关键词', index=False)
print("统计结果已导出到 danmu_statistics.xlsx")
# 在生成词云图时进行分词
generate_wordcloud(df_danmu)
def generate_wordcloud(df_danmu):
# 进行分词
processed_texts = []
for text in df_danmu['弹幕']:
words = jieba.cut(text) # 使用 jieba 分词
processed_texts.append(' '.join(words)) # 分词结果拼接为字符串
# 创建词云图的文本数据
text = ' '.join(processed_texts)
# 生成词云图
wordcloud = WordCloud(font_path='simhei.ttf', width=800, height=600, background_color='white').generate(text)
# 显示词云图
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
def main():
query = '2024巴黎奥运会'
total_results = 300 # 设定要爬取的总视频数量
video_list = search_bilibili(query, total_results)
all_danmu_texts = []
for bvid in video_list:
cid = get_video_cid(bvid)
if cid:
danmu_texts = fetch_danmu(cid)
all_danmu_texts.extend(danmu_texts)
count_and_rank_danmu(all_danmu_texts)
if __name__ == '__main__':
main()
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