diff --git a/巴黎奥运弹幕分析.py b/巴黎奥运弹幕分析.py
new file mode 100644
index 0000000..e2caebb
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+++ b/巴黎奥运弹幕分析.py
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+import requests
+import pandas as pd
+from bs4 import BeautifulSoup
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import re
+import collections
+
+# 2.1 数据获取
+headers = {
+    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36'
+}
+search_url = 'https://search.bilibili.com/all?keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A'
+response = requests.get(search_url, headers=headers)
+html_content = response.text
+soup = BeautifulSoup(html_content, 'html.parser')
+
+video_links = []
+for link in soup.find_all('a', class_='title')[:300]:
+    video_links.append('https:' + link['href'])
+
+def get_danmaku(url):
+    danmaku_list = []
+    # 这里假设获取弹幕的逻辑，可以根据实际情况修改
+    return danmaku_list
+
+all_danmaku = []
+for link in video_links:
+    danmaku = get_danmaku(link)
+    all_danmaku.extend(danmaku)
+
+# 2.2 数据统计
+ai_keywords = ["AI", "人工智能", "AI 技术"]
+danmaku_count = collections.defaultdict(int)
+for danmaku in all_danmaku:
+    for keyword in ai_keywords:
+        if keyword in danmaku:
+            danmaku_count[danmaku] += 1
+
+data = {'弹幕内容': list(danmaku_count.keys()), '数量': list(danmaku_count.values())}
+df = pd.DataFrame(data)
+sorted_df = df.sort_values(by='数量', ascending=False)
+top_8_df = sorted_df.head(8)
+
+excel_filename = 'ai_danmaku_result.xlsx'
+top_8_df.to_excel(excel_filename, index=False)
+
+# 2.3 数据可视化
+text = " ".join(top_8_df['弹幕内容'])
+
+background_image = np.array(Image.open('background.jpg'))
+wordcloud = WordCloud(width=800, height=400, background_color='white', mask=background_image, font_path='your_font.ttf').generate(text)
+image_colors = ImageColorGenerator(background_image)
+plt.figure(figsize=(10, 5))
+plt.imshow(wordcloud.recolor(color_func=image_colors), interpolation='bilinear')
+plt.axis('off')
+plt.savefig('wordcloud.png')
+plt.show()
+
+# 2.4 数据结论
+# 根据统计数据和观察词云图，可以得出以下主流看法：
+if top_8_df is not None and len(top_8_df) > 0:
+    common_comments = top_8_df['弹幕内容'].tolist()
+    print(f"当前 B 站用户对于 2024 巴黎奥运会应用 AI 技术的主流看法可能包括：{common_comments}等方面的关注和讨论。")
+else:
+    print("未发现明显的主流看法。")
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