diff --git a/get_wordcloud_pic.py b/get_wordcloud_pic.py
new file mode 100644
index 0000000..70eb4bb
--- /dev/null
+++ b/get_wordcloud_pic.py
@@ -0,0 +1,56 @@
+import pandas as pd
+import jieba
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+
+# 1. 读取停用词表
+def load_stopwords(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        stopwords = set(line.strip() for line in f)
+    return stopwords
+
+# 2. 过滤停用词
+def remove_stopwords(words_list, stopwords):
+    return [word for word in words_list if word not in stopwords and len(word) > 1]
+
+# 3. 读取Excel文件并提取弹幕内容
+file_path = "danmu_data.xlsx"
+df = pd.read_excel(file_path)
+comments = df['danmu'].astype(str)
+text = ' '.join(comments)
+
+# 4. 使用 jieba 分词
+words = jieba.cut(text, cut_all=False)
+
+# 5. 加载停用词表
+stopwords_file = "D://edge//stop.txt"  # 替换为实际路径
+stopwords = load_stopwords(stopwords_file)
+
+# 6. 去除停用词
+filtered_words = remove_stopwords(words, stopwords)
+
+# 7. 将过滤后的词汇重新拼接为一个字符串
+words_list = ' '.join(filtered_words)
+
+# 8. 加载形状图片并生成词云
+mask = np.array(Image.open("D://edge//kk.png"))
+
+wordcloud = WordCloud(
+    font_path='simhei.ttf',  # 确保支持中文
+    background_color='white',
+    mask=mask,
+    contour_width=1,
+    contour_color='black',
+    width=800,
+    height=600
+).generate(words_list)
+
+# 9. 显示词云图
+plt.imshow(wordcloud, interpolation='bilinear')
+plt.axis("off")
+plt.show()
+
+# 10. 保存词云图
+wordcloud.to_file("filtered_wordcloud.png")