diff --git a/test_script_2.2.2.py b/test_script_2.2.2.py
new file mode 100644
index 0000000..661d3bd
--- /dev/null
+++ b/test_script_2.2.2.py
@@ -0,0 +1,50 @@
+import unittest
+from unittest.mock import patch
+import jieba
+import wordcloud
+import pandas as pd
+
+class TestWordCloudGeneration(unittest.TestCase):
+
+    @patch('pandas.read_excel')
+    @patch('wordcloud.WordCloud.to_file')
+    def test_wordcloud_generation(self, mock_to_file, mock_read_excel):
+        # 模拟读取的 Excel 数据
+        mock_df = pd.DataFrame({
+            'AI': ['人工智能在发展', 'AI改变世界'],
+            'Machine learning': ['机器学习是AI的基础', '深度学习是其中之一']
+        })
+        mock_read_excel.return_value = mock_df
+
+        # 引入你需要测试的代码
+        df = pd.read_excel('top_ai_danmakus.xlsx')
+
+        # 将Excel所有列的数据拼接成一个长字符串
+        book = ''
+        for column in df.columns:
+            book += ' '.join(df[column].astype(str)) + ' '
+
+        # 使用jieba进行分词
+        book_list = jieba.lcut(book)
+        book_str = ' '.join(book_list)
+
+        # 创建词云对象
+        wc = wordcloud.WordCloud(
+            width=500,
+            height=500,
+            background_color='white',
+            stopwords={'main', 'Taipei', 'afraid', 'aiden', 'Britain'},  # 可添加不需要的停用词
+            font_path='msyh.ttc'  # 设置中文字体路径
+        )
+
+        # 生成词云
+        wc.generate(book_str)
+
+        # 检查是否调用了保存词云的函数
+        wc.to_file('词云图.png')
+
+        # 验证 to_file 函数是否被调用
+        mock_to_file.assert_called_once_with('词云图.png')
+
+if __name__ == '__main__':
+    unittest.main()