diff --git a/english.py b/english.py
new file mode 100644
index 0000000..73294b9
--- /dev/null
+++ b/english.py
@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 24 23:23:07 2025
+
+@author: 15733
+"""
+
+import requests
+import re
+import csv
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# 1. 爬取数据
+def scrape_company_data():
+    headers = {
+        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.69 Safari/537.36"
+    }
+    message = []
+    
+    # 总共16个页面的数据
+    for page in range(16):
+        # 组装url
+        if page == 0:
+            url = "https://top.chinaz.com/gongsitop/index_500top.html"
+        else:
+            url = f"https://top.chinaz.com/gongsitop/index_500top_{page + 1}.html"
+        
+        try:
+            response = requests.get(url, headers=headers)
+            response.raise_for_status()  # 检查请求是否成功
+            html = response.text
+            
+            # 使用正则表达式提取数据
+            company = re.findall('<a.*?target="_blank">(.+?)</a></h3>', html)
+            person = re.findall('法定代表人：</span>(.*?)</p>', html)
+            signDate = re.findall('注册时间：</span>(.*?)</p>', html)
+            category = re.findall('证券类别：</span>(.*?)</p>', html)
+            
+            # 确保每页数据长度一致
+            min_length = min(len(company), len(person), len(signDate), len(category))
+            page_data = list(zip(
+                company[:min_length], 
+                person[:min_length], 
+                signDate[:min_length], 
+                category[:min_length]
+            ))
+            
+            message.extend(page_data)
+            print(f"第{page+1}页数据爬取完成，共{len(page_data)}条记录")
+            
+        except Exception as e:
+            print(f"第{page+1}页爬取失败: {e}")
+            continue
+    
+    return message
+
+# 2. 保存数据到CSV
+def save_to_csv(data, filename="china_top500_companies.csv"):
+    with open(filename, "w", newline="", encoding="utf-8-sig") as f:
+        writer = csv.writer(f)
+        writer.writerow(["公司名", "法定代表人", "注册时间", "证券类别"])  # 写入标题行
+        writer.writerows(data)
+    print(f"数据已保存到 {filename}")
+
+# 3. 分析证券占比并绘制饼图
+def analyze_and_plot(filename="china_top500_companies.csv"):
+    # 读取数据
+    df = pd.read_csv(filename)
+    
+    # 统计证券类别占比
+    category_counts = df["证券类别"].value_counts()
+    
+    # 设置中文字体
+    plt.rcParams['font.sans-serif'] = ['SimHei']
+    plt.rcParams['axes.unicode_minus'] = False
+    
+    # 绘制饼图
+    plt.figure(figsize=(10, 8))
+    plt.pie(category_counts, 
+            labels=category_counts.index, 
+            autopct='%1.1f%%',
+            startangle=90,
+            textprops={'fontsize': 12})
+    plt.title("中国500强公司证券类别占比", fontsize=16)
+    plt.axis('equal')  # 保证饼图是圆形
+    
+    # 保存图片
+    plt.savefig("证券类别占比.png", dpi=300, bbox_inches='tight')
+    print("证券类别占比分析图已保存为 证券类别占比.png")
+    plt.show()
+    
+    return category_counts
+
+# 主程序
+if __name__ == "__main__":
+    # 1. 爬取数据
+    company_data = scrape_company_data()
+    
+    # 2. 保存到CSV
+    save_to_csv(company_data)
+    
+    # 3. 分析并绘制饼图
+    category_distribution = analyze_and_plot()
+    print("\n证券类别分布统计:")
+    print(category_distribution)
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