diff --git a/wordcloud1.py b/wordcloud1.py
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+++ b/wordcloud1.py
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+import pandas as pd
+from PIL import Image
+import numpy as np
+import jieba
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+
+
+def generate_beautiful_wordcloud(file_name='ai_danmaku_statistics.xlsx', mask_image_path=None,font_path=r"C:\Users\wyk93\Desktop\苹方黑体-准-简.ttf"):
+    # 读取 Excel 文件
+    df = pd.read_excel(file_name)
+
+    # 合并所有弹幕内容为一个大字符串
+    text = ' '.join(df['弹幕内容'].dropna().astype(str))
+
+    # 使用 jieba 进行分词
+    seg_list = jieba.cut(text, cut_all=True)
+    segmented_text = " ".join(seg_list)
+
+    # # 打印合并后的文本以检查内容
+    # print("Text content:")
+    # print(text[:500])  # 打印前500个字符以检查
+
+    # 读取自定义形状图片
+    if mask_image_path:
+        mask_image = np.array(Image.open(mask_image_path).convert('L'))
+        mask_image[mask_image > 200] = 255
+        mask_image[mask_image <= 200] = 0
+    else:
+        mask_image = None
+
+    background_image = Image.open(mask_image_path)
+
+    # 创建词云对象
+    wordcloud = WordCloud(
+        background_color='white',# 透明背景
+        mask=mask_image,
+        contour_color=None,
+        contour_width=2,
+        width=2000,  # 词云图的宽度
+        height=2000,  # 词云图的高度
+        colormap='viridis' , # 使用 'viridis' 颜色映射
+        max_words=20000,
+        # min_font_size=10,
+        # max_font_size=100,
+        # prefer_horizontal=0.9,  # 更高的水平优先级
+        font_path=font_path # 如果需要显示中文，需要指定中文字体路径
+    ).generate(segmented_text)  # 这里替换为你自己的文本数据
+
+    # 显示词云图
+    plt.figure(figsize=(10, 8))
+    plt.imshow(background_image, interpolation='bilinear')
+    plt.imshow(wordcloud, interpolation='bilinear', alpha=0.4)  # 设置透明度
+    plt.axis('off')  # 不显示坐标轴
+    plt.show()
+
+# 调用函数生成词云
+# 可选: 使用自定义形状的掩膜图像
+generate_beautiful_wordcloud(mask_image_path=r"C:\Users\wyk93\Desktop\图片素材\sucai3.jpg")
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