From 89bc73b2ddd8ea1f9ccb049ba9aae715b48d2eb9 Mon Sep 17 00:00:00 2001
From: fzu102301136 <3225314707@qq.com>
Date: Sun, 16 Nov 2025 22:09:37 +0800
Subject: [PATCH] ADD file via upload

---
 main.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 main.py

diff --git a/main.py b/main.py
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index 0000000..93522f9
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+++ b/main.py
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+from spider import BilibiliSpider  # 爬虫模块，获取弹幕数据
+from analyzer import DanmukuAnalyzer  # 弹幕分析模块，处理和分析弹幕数据
+from basic_visualizer import BasicVisualizer  # 基础可视化模块，生成统计表格和基础图表
+from wordcloud_visualizer import WordCloudVisualizer  # 词云模块，生成弹幕词云
+from view_visualizer import ViewDistributionVisualizer  # 观点分布模块，展示不同观点的分布情况
+
+
+def main():
+    spider = BilibiliSpider(total_videos=300)#设置爬取的视频总数为300个
+    danmukus = spider.crawl_danmukus(keywords=["大语言模型"])# 爬取"大语言模型"关键词的视频的弹幕数据
+    if not danmukus:
+        print("无弹幕数据，终止程序")
+        return#没有获得弹幕就停下来
+
+    # 2. 分析数据：对爬取的弹幕进行处理和分析
+    analyzer = DanmukuAnalyzer(danmukus)
+    ai_counter = analyzer.get_ai_related()
+    view_counts = analyzer.classify_views(ai_counter)
+
+    # 3. 基础统计可视化：生成基础统计结果并可视化
+    BasicVisualizer.save_to_excel(ai_counter)
+    BasicVisualizer.plot_top8(ai_counter)
+
+    # 4. 词云可视化（增强过滤）：生成弹幕词云并保存
+    wc_visualizer = WordCloudVisualizer()
+
+    wc_visualizer.generate(
+        danmukus,
+        stopwords=analyzer.stopwords,  # 使用分析器中的停用词表过滤无意义词汇
+        min_length=4,  # 过滤长度小于2的词
+        min_freq=5,  # 过滤出现次数小于3的词
+        filename="弹幕词云.png"  # 词云图片保存路径
+    )
+
+    # 5. 观点分布可视化：展示不同观点的分布情况）
+    ViewDistributionVisualizer.plot(view_counts)
+    ViewDistributionVisualizer.print_stats(view_counts)
+
+    # 6. 打印前8弹幕文字结果：在控制台输出排名前8的AI相关弹幕
+    print("\n===== AI相关弹幕Top8 =====")
+    # 遍历出现次数最多的前8条弹幕，按格式输出
+    for i, (text, count) in enumerate(ai_counter.most_common(8), 1):
+        print(f"{i}. {text}: {count}次")
+
+if __name__ == "__main__":
+    main()
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