# -*- coding: utf-8 -*- """ 金融机构名字爬虫增强模块 支持通过机构名称爬取网站信息，包含反爬、翻译和双语展示功能 """ import json import re import time import random import hashlib from pathlib import Path from typing import Dict, Any, List, Optional, Tuple from datetime import datetime # Scrapling导入 try: from scrapling.fetchers import StealthyFetcher, DynamicFetcher from scrapling.parser import Selector SCRAPLING_AVAILABLE = True except ImportError: SCRAPLING_AVAILABLE = False Selector = Any # type: ignore # forward reference fallback try: from playwright.sync_api import sync_playwright PLAYWRIGHT_AVAILABLE = True except ImportError: PLAYWRIGHT_AVAILABLE = False # 数据目录 SKILL_DATA_DIR = Path(__file__).parent.parent / "data" # 常用URL模式（从机构名称推断官网） URL_PATTERNS = { # 政策性银行 "国家开发银行": "https://www.cdb.com.cn", # 中国进出口银行（官网为政府域名，已移除） "中国农业发展银行": "https://www.adbc.com.cn", # 国有商业银行 "工商银行": "https://www.icbc.com.cn", "建设银行": "https://www.ccb.com", "农业银行": "https://www.abchina.com", "中国银行": "https://www.boc.cn", "交通银行": "https://www.bankcomm.com", "邮储银行": "https://www.psbc.com", # 股份制银行 "招商银行": "https://www.cmbchina.com", "中信银行": "https://www.citicbank.com", "浦发银行": "https://www.spdb.com.cn", "兴业银行": "https://www.cib.com.cn", "民生银行": "https://www.cmbc.com.cn", "平安银行": "https://bank.pingan.com", "光大银行": "https://www.cebbank.com", "华夏银行": "https://www.hxb.com.cn", "广发银行": "https://www.cgbchina.com.cn", "北京银行": "https://www.bankofbeijing.com.cn", "上海银行": "https://www.bankofshanghai.com", "江苏银行": "https://www.jsbchina.cn", "南京银行": "https://www.njcb.com.cn", "杭州银行": "https://www.hzbank.com.cn", "宁波银行": "https://www.nbbank.com.cn", # 保险公司 "中国人寿": "https://www.chinalife.com.cn", "中国平安": "https://www.pingan.com.cn", "中国太保": "https://www.cpic.com.cn", "中国人保": "https://www.picc.com.cn", "新华保险": "https://www.newchina-life.com", "泰康保险": "https://www.taikang.com", "友邦保险": "https://www.aia.com.cn", # 基金公司 "易方达": "https://www.efunds.com.cn", "华夏基金": "https://www.chinaamc.com", "广发基金": "https://www.gffunds.com.cn", "嘉实基金": "https://www.jsfund.cn", "南方基金": "https://www.nffund.com.cn", "博时基金": "https://www.bosera.com", "招商基金": "https://www.cmfchina.com", "工银基金": "https://www.icbcfs.com", "建信基金": "https://www.ccbfund.cn", "富国基金": "https://www.fullgoal.com.cn", "鹏华基金": "https://www.phfund.com.cn", "汇添富基金": "https://www.htffund.com", "中欧基金": "https://www.zofund.com", "兴证全球基金": "https://www.xqglobal.com", # 证券公司 "中信证券": "https://www.cs.ecitic.com", "中信建投证券": "https://www.csc108.com", "国泰君安证券": "https://www.gtja.com", "华泰证券": "https://www.htsc.com.cn", "广发证券": "https://www.gf.com.cn", "招商证券": "https://www.newone.com.cn", "海通证券": "https://www.htsec.com", "国信证券": "https://www.guosen.com.cn", "东方证券": "https://www.dfzq.com.cn", "兴业证券": "https://www.xyzq.com.cn", "银河证券": "https://www.chinastock.com.cn", "长江证券": "https://www.95579.com", "中金公司": "https://www.cicc.com", "光大证券": "https://www.ebscn.com", "平安证券": "https://stock.pingan.com", "方正证券": "https://www.foundersc.com", # 信托公司 "中信信托": "https://www.zxxt.com.cn", "平安信托": "https://www.paxt.com.cn", "中融信托": "https://www.zhongrongtrust.com", "华润信托": "https://www.crhtrust.com.cn", "外贸信托": "https://www.fotacn.com", # 外资银行 "汇丰银行": "https://www.hsbc.com.cn", "渣打银行": "https://www.standardchartered.com.cn", "花旗银行": "https://www.citibank.com.cn", "摩根大通银行": "https://www.jpmorganchina.com.cn", "高盛集团": "https://www.goldmansachs.com.cn", "瑞银证券": "https://www.ubs.com/cn/zh.html", "野村证券": "https://www.nomura.com", # 外资保险公司 "安盛保险": "https://www.axa.com.cn", "安联保险": "https://www.allianz.com.cn", "友邦保险": "https://www.aia.com.cn", # 外资资产管理公司 "贝莱德资产管理": "https://www.blackrock.com.cn", "瑞银资产管理": "https://www.ubs.com/global/en/asset-management.html", } # 外资机构名称映射（用于翻译） FOREIGN_INSTITUTION_TRANSLATIONS = { "汇丰银行": "HSBC Holdings plc", "渣打银行": "Standard Chartered plc", "花旗银行": "Citibank N.A.", "摩根大通银行": "JPMorgan Chase & Co.", "摩根士丹利": "Morgan Stanley", "高盛集团": "Goldman Sachs Group Inc.", "德意志银行": "Deutsche Bank AG", "瑞士银行": "UBS Group AG", "法国巴黎银行": "BNP Paribas SA", "法兴银行": "Société Générale SA", "东方汇理银行": "Crédit Agricole CIB", "瑞穗银行": "Mizuho Financial Group", "三菱日联金融": "Mitsubishi UFJ Financial Group", "三井住友金融": "Sumitomo Mitsui Financial Group", "友利银行": "Woori Bank", "韩亚银行": "Hana Bank", "新韩银行": "Shinhan Bank", "星展银行": "DBS Bank", "华侨银行": "OCBC Bank", "大华银行": "UOB Bank", "东亚银行": "Bank of East Asia", "恒生银行": "Hang Seng Bank", "安盛保险": "AXA SA", "安联保险": "Allianz SE", "忠利保险": "Generali Group", "保德信保险": "Prudential Financial Inc.", "大都会保险": "MetLife Inc.", "友邦保险": "AIA Group Limited", "宏利保险": "Manulife Financial", "安达保险": "Chubb Limited", "信利保险": "Zurich Insurance Group", "英杰华保险": "Aviva", "耆卫保险": "Old Mutual", "日本生命保险": "Nippon Life Insurance", "第一生命保险": "Dai-ichi Life Insurance", "明治安田保险": "Meiji Yasuda Life Insurance", "野村证券": "Nomura Holdings Inc.", "大和证券": "Daiwa Securities Group Inc.", "瑞穗证券": "Mizuho Securities", "三菱日联证券": "Mitsubishi UFJ Securities", "高盛高华证券": "Goldman Sachs Gao Hua", "汇丰前海证券": "HSBC Qianhai Securities", "法巴证券": "BNP Paribas Securities", "法兴证券": "Société Générale Securities", "东方汇理期货": "Crédit Agricole CIB Futures", "瑞银期货": "UBS Futures", "汇丰资产管理": "HSBC Global Asset Management", "瑞银资产管理": "UBS Asset Management", "摩根资产管理": "JPMorgan Asset Management", "贝莱德资产管理": "BlackRock", "道富环球投资管理": "State Street Global Advisors", "富达国际资产管理": "Fidelity International", "景顺资产管理": "Invesco", "安联资产管理": "Allianz Global Investors", } # 反爬设置 DEFAULT_DELAY = (1.5, 3.5) # 随机延迟范围（秒） MAX_RETRIES = 2 REQUEST_TIMEOUT = 30000 # 30秒 class AntiCrawlFetcher: """带反爬功能的爬取器""" def __init__(self): self.fetcher = None self.last_request_time = 0 self.request_count = 0 self.failed_domains = set() # 记录失败域名，避免重复请求 def _random_delay(self): """随机延迟""" delay = random.uniform(*DEFAULT_DELAY) time.sleep(delay) def _update_last_request(self): """更新最后请求时间""" self.last_request_time = time.time() self.request_count += 1 def fetch(self, url: str, use_dynamic: bool = False, headers: Dict = None, retry_count: int = 0) -> Optional[Selector]: """ 带反爬的爬取 Args: url: 目标URL use_dynamic: 是否使用动态渲染 headers: 自定义请求头 retry_count: 当前重试次数 Returns: Selector对象或None """ if not SCRAPLING_AVAILABLE: return None self._random_delay() try: if self.fetcher is None: self.fetcher = StealthyFetcher() # 构建请求头 request_headers = { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Accept-Encoding": "gzip, deflate, br", "DNT": "1", "Connection": "keep-alive", "Upgrade-Insecure-Requests": "1", } if headers: request_headers.update(headers) # 尝试使用动态爬取 if use_dynamic: try: page = DynamicFetcher.fetch( url, headless=True, network_idle=True, solve_cloudflare=True, timeout=REQUEST_TIMEOUT ) self._update_last_request() # scrapling 0.2.x 兼容 if hasattr(page, 'html_content'): page.html = page.html_content elif hasattr(page, 'body') and isinstance(page.body, str): page.html = page.body return page except Exception as e: if retry_count < MAX_RETRIES: wait_time = random.uniform(5, 15) print(f"[反爬] 动态爬取失败，{wait_time:.1f}秒后重试: {url}") time.sleep(wait_time) return self.fetch(url, use_dynamic, headers, retry_count + 1) else: print(f"[反爬] 动态爬取最终失败: {url}") return None # 使用隐式请求（默认） page = self.fetcher.fetch( url, headless=True, solve_cloudflare=True, timeout=REQUEST_TIMEOUT ) self._update_last_request() # scrapling 0.2.x 兼容：注入 .html 属性 if hasattr(page, 'html_content'): page.html = page.html_content elif hasattr(page, 'body') and isinstance(page.body, str): page.html = page.body return page except Exception as e: print(f"[反爬] 爬取失败 ({retry_count + 1}/{MAX_RETRIES + 1}): {url}") print(f"[错误] {str(e)[:100]}") if retry_count < MAX_RETRIES: wait_time = random.uniform(5, 15) print(f"[反爬] {wait_time:.1f}秒后重试...") time.sleep(wait_time) return self.fetch(url, use_dynamic, headers, retry_count + 1) return None class InstitutionNameScraper: """通过机构名称爬取网站内容的爬取器""" def __init__(self): self.fetcher = AntiCrawlFetcher() self._institution_cache = {} # 机构信息缓存 self._load_institution_data() def _load_institution_data(self): """加载所有机构数据""" data_files = [ "institutions.json", "foreign_institution_list.json", "policy_bank_list.json", "state_owned_bank_list.json", "joint_stock_bank_list.json", "city_commercial_bank_list.json", "rural_commercial_bank_list.json", "insurance_list.json", "fund_company_list.json", "securities_list.json", "trust_company_list.json", "private_fund_list.json", "financial_lease_list.json", "city_investment_list.json", ] for filename in data_files: filepath = SKILL_DATA_DIR / filename if filepath.exists(): try: with open(filepath, 'r', encoding='utf-8') as f: data = json.load(f) # 提取institutions数组 if "institutions" in data: for inst in data["institutions"]: self._institution_cache[inst.get("name", "")] = { **inst, "source_file": filename } except Exception as e: print(f"[警告] 加载{filename}失败: {e}") def find_institution(self, name: str) -> Optional[Dict]: """ 通过名称查找机构信息 Args: name: 机构名称（支持模糊匹配） Returns: 机构信息字典 """ if not name: return None # 精确匹配 if name in self._institution_cache: return self._institution_cache[name] # 模糊匹配 name_lower = name.lower() for inst_name, inst_info in self._institution_cache.items(): if name_lower in inst_name.lower() or inst_name.lower() in name_lower: return inst_info # 关键词匹配 keywords = name.replace("基金", "").replace("证券", "").replace("银行", "").replace("保险", "").strip() if keywords and len(keywords) >= 2: for inst_name, inst_info in self._institution_cache.items(): if keywords in inst_name or inst_name in keywords: return inst_info return None def get_institution_url(self, name: str) -> Optional[str]: """ 获取机构官网URL Args: name: 机构名称 Returns: 官网URL或None """ # 优先从预定义URL模式获取 if name in URL_PATTERNS: return URL_PATTERNS[name] # 从URL模式中查找包含关键词的 for pattern_name, url in URL_PATTERNS.items(): if name in pattern_name or pattern_name in name: return url return None def is_foreign_institution(self, name: str) -> bool: """判断是否为外资机构""" return name in FOREIGN_INSTITUTION_TRANSLATIONS def get_english_name(self, name: str) -> Optional[str]: """获取机构英文名称""" return FOREIGN_INSTITUTION_TRANSLATIONS.get(name) def scrape_by_name(self, name: str, use_dynamic: bool = True) -> Dict[str, Any]: """ 通过机构名称爬取网站内容 Args: name: 机构名称 use_dynamic: 是否使用动态渲染 Returns: 爬取结果字典 """ result = { "name": name, "scrape_time": time.strftime("%Y-%m-%d %H:%M:%S"), "url": None, "success": False, "content": {}, "error": None, "is_foreign": self.is_foreign_institution(name), "english_name": self.get_english_name(name), } # 查找机构信息 inst_info = self.find_institution(name) if inst_info: result["institution_info"] = inst_info # 获取URL url = self.get_institution_url(name) if not url: result["error"] = f"未找到机构'{name}'的官网URL" return result result["url"] = url print(f"[爬虫] 正在爬取: {name} -> {url}") # 爬取内容 page = self.fetcher.fetch(url, use_dynamic=use_dynamic) if page is None: result["error"] = "爬取失败，请检查网络或URL是否可访问" return result result["success"] = True # 提取页面内容 content = self._extract_page_content(page, url) result["content"] = content # 如果是外资机构，处理双语展示 if result["is_foreign"]: result["bilingual_content"] = self._generate_bilingual_content(content, name) return result def _extract_page_content(self, page: Selector, url: str) -> Dict[str, Any]: """提取页面内容""" content = { "title": "", "main_content": "", "about": "", "products": [], "contact": {}, } try: # 提取标题 for sel in ["h1", ".site-title", "[class*='title']", "title"]: try: el = page.css_first(sel) if el: content["title"] = el.text().strip()[:200] break except: continue # 如果没拿到标题，尝试从meta获取 if not content["title"]: try: meta_title = page.re_search(r']*>([^<]+)') if meta_title: content["title"] = meta_title.group(1).strip() except: pass # 提取主要文本内容 text_selectors = [ "main", "article", ".main-content", "[class*='content']", "[class*='about']", ".container", ] for sel in text_selectors: try: el = page.css_first(sel) if el: text = el.text().strip() if len(text) > 100: # 至少100字符 content["main_content"] = text[:3000] # 限制长度 break except: continue # 提取关于我们/简介 about_patterns = [ r'(?:关于我们|关于我们|公司简介|Company Profile|About Us)[：:\s]*([^\n]{100,500})', r'(?:我们是谁|Our Story)[：:\s]*([^\n]{50,300})', ] for pattern in about_patterns: match = page.re_search(pattern) if match: content["about"] = match.group(1).strip() break # 如果还没获取到about，尝试从main_content中提取 if not content["about"] and content["main_content"]: about_match = re.search(r'(?:关于我们|关于我们|公司简介)[：:\s]*([^\n]{50,300})', content["main_content"]) if about_match: content["about"] = about_match.group(1).strip() # 提取联系信息 contact_patterns = { "phone": r'(\d{3,4}[-\s]?\d{7,8}|\d{3}[-\s]?\d{4}[-\s]?\d{4})', "email": r'([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})', "address": r'(?:地址|Address)[：:\s]*([^\n]{10,100})', } for key, pattern in contact_patterns.items(): match = page.re_search(pattern) if match: content["contact"][key] = match.group(1).strip() except Exception as e: print(f"[警告] 内容提取失败: {e}") return content def _generate_bilingual_content(self, content: Dict, name: str) -> Dict[str, Any]: """生成双语内容""" bilingual = { "institution": name, "english_name": self.get_english_name(name), "title": {}, "about": {}, "main_content": {}, } # 标题翻译（简化处理，实际项目中可接入翻译API） if content.get("title"): bilingual["title"] = { "zh": content["title"], "en": self._translate_to_english(content["title"], name), } # 关于信息双语 if content.get("about"): bilingual["about"] = { "zh": content["about"], "en": self._translate_to_english(content["about"], name), } # 主要内容双语 if content.get("main_content"): bilingual["main_content"] = { "zh": content["main_content"][:1500] if len(content["main_content"]) > 1500 else content["main_content"], "en": self._translate_to_english(content["main_content"][:1500] if len(content["main_content"]) > 1500 else content["main_content"], name), } return bilingual def _translate_to_english(self, text: str, institution_name: str) -> str: """ 将中文文本翻译为英文（简化版本） 实际项目中应接入专业翻译API（如百度翻译、Google翻译等） """ if not text: return "" # 简短翻译映射（实际项目中应接入翻译API） translation_mapping = { "关于我们": "About Us", "公司简介": "Company Profile", "联系我们": "Contact Us", "产品中心": "Products", "新闻中心": "News", "投资者关系": "Investor Relations", "人才招聘": "Careers", "企业文化": "Corporate Culture", "发展历程": "Our Story", "组织架构": "Organization", "社会责任": "Corporate Social Responsibility", "公司地址": "Address", "联系电话": "Tel", "电子邮箱": "Email", } # 检查是否是简短文本 for zh, en in translation_mapping.items(): if zh in text[:10]: return en # 对于长文本，返回原文+说明（实际应接入翻译API） if len(text) > 100: return f"[English Translation of Chinese Text]\n{text[:500]}..." return f"[Translated] {text}" def format_bilingual_display(self, result: Dict[str, Any]) -> str: """ 格式化双语展示内容 Args: result: 爬取结果 Returns: 格式化字符串 """ if not result.get("success"): return f"❌ 爬取失败: {result.get('error', '未知错误')}" name = result.get("name", "") english_name = result.get("english_name", "") output = [] output.append(f"\n{'='*60}") output.append(f"🏦 机构名称: {name}") if english_name: output.append(f"🌐 English: {english_name}") output.append(f"{'='*60}\n") # 外资机构双语展示 if result.get("is_foreign") and result.get("bilingual_content"): bc = result["bilingual_content"] output.append("【公司简介 / Company Profile】\n") if bc.get("about"): about = bc["about"] output.append(f"🇨🇳 中文: {about.get('zh', '')}") output.append(f"🇺🇸 EN: {about.get('en', '')}") else: output.append("(暂无公司简介 / No profile available)") output.append(f"\n{'─'*60}") if bc.get("main_content"): content = bc["main_content"] output.append("\n【主要内容 / Main Content】\n") output.append(f"🇨🇳 中文:\n{content.get('zh', '')}") output.append(f"\n🇺🇸 EN:\n{content.get('en', '')}") else: # 国内机构单语展示 content = result.get("content", {}) if content.get("title"): output.append(f"📌 标题: {content['title']}\n") if content.get("about"): output.append(f"\n📋 关于我们:\n{content['about']}") if content.get("main_content"): output.append(f"\n📄 主要内容:\n{content['main_content'][:800]}...") if content.get("contact"): output.append(f"\n📞 联系方式:") for k, v in content["contact"].items(): output.append(f" {k}: {v}") output.append(f"\n{'='*60}") output.append(f"🕐 爬取时间: {result.get('scrape_time', '')}") output.append(f"🔗 来源: {result.get('url', '')}") output.append(f"{'='*60}") return "\n".join(output) def scrape_institution_by_name(name: str, use_dynamic: bool = True) -> Dict[str, Any]: """ 通过机构名称爬取信息的便捷函数 Args: name: 机构名称 use_dynamic: 是否使用动态渲染 Returns: 爬取结果 """ scraper = InstitutionNameScraper() result = scraper.scrape_by_name(name, use_dynamic=use_dynamic) return result def scrape_institution_by_url(url: str, use_dynamic: bool = True) -> Dict[str, Any]: """ 通过URL直接爬取网站内容 Args: url: 目标URL use_dynamic: 是否使用动态渲染 Returns: 爬取结果 """ result = { "scrape_time": time.strftime("%Y-%m-%d %H:%M:%S"), "url": url, "success": False, "content": {}, "error": None, "is_foreign": False, "english_name": None, } # 从URL推断机构名称 url_lower = url.lower() inferred_name = None # 尝试从URL域名匹配已知机构 for name, pattern_url in URL_PATTERNS.items(): if pattern_url.lower() in url_lower or url_lower in pattern_url.lower(): inferred_name = name break # 检查是否是外资机构 if inferred_name: result["is_foreign"] = is_foreign_institution(inferred_name) result["english_name"] = FOREIGN_INSTITUTION_TRANSLATIONS.get(inferred_name) result["inferred_name"] = inferred_name # 爬取 fetcher = AntiCrawlFetcher() page = fetcher.fetch(url, use_dynamic=use_dynamic) if page is None: result["error"] = "爬取失败，请检查URL或网络连接" return result result["success"] = True # 提取内容 scraper = InstitutionNameScraper() content = scraper._extract_page_content(page, url) result["content"] = content # 如果是外资机构，处理双语展示 if result["is_foreign"]: result["bilingual_content"] = scraper._generate_bilingual_content(content, inferred_name or url) return result def is_foreign_institution(name: str) -> bool: """判断是否为外资机构""" return name in FOREIGN_INSTITUTION_TRANSLATIONS def scrape_institution(input_str: str, use_dynamic: bool = True) -> Dict[str, Any]: """ 智能爬取入口 - 自动识别输入是URL还是机构名称 Args: input_str: URL或机构名称 use_dynamic: 是否使用动态渲染 Returns: 爬取结果 """ # 判断是URL还是名称 is_url = ( "http://" in input_str.lower() or "https://" in input_str.lower() or ".com" in input_str.lower() or ".cn" in input_str.lower() or ".org" in input_str.lower() or "/" in input_str and len(input_str) > 20 # 简单判断URL格式 ) if is_url: return scrape_institution_by_url(input_str, use_dynamic=use_dynamic) else: return scrape_institution_by_name(input_str, use_dynamic=use_dynamic) def format_scraping_result(result: Dict[str, Any], bilingual: bool = True) -> str: """ 格式化爬取结果 Args: result: 爬取结果 bilingual: 是否双语展示 Returns: 格式化字符串 """ scraper = InstitutionNameScraper() if bilingual and result.get("is_foreign"): return scraper.format_bilingual_display(result) else: # 非双语格式 if not result.get("success"): return f"❌ 爬取失败: {result.get('error', '未知错误')}" output = [] output.append(f"\n🏦 {result.get('name', '')}") output.append(f"🔗 {result.get('url', '')}\n") content = result.get("content", {}) if content.get("title"): output.append(f"📌 标题: {content['title']}\n") if content.get("about"): output.append(f"📋 关于: {content['about'][:300]}...") if content.get("main_content"): output.append(f"📄 内容: {content['main_content'][:500]}...") return "\n".join(output) # CLI入口 if __name__ == "__main__": import sys if len(sys.argv) < 2: print("用法:") print(" python name_scraper.py <机构名称> # 爬取单个机构") print(" python name_scraper.py --list <名称列表> # 批量爬取") print(" python name_scraper.py --search <关键词> # 搜索机构") print("\n示例:") print(' python name_scraper.py "汇丰银行"') print(' python name_scraper.py "易方达基金"') print(' python name_scraper.py --search "基金"') sys.exit(1) scraper = InstitutionNameScraper() if sys.argv[1] == "--search": # 搜索机构 if len(sys.argv) < 3: print("请提供搜索关键词") sys.exit(1) keyword = sys.argv[2] print(f"\n搜索关键词: {keyword}") print("="*50) for inst_name in sorted(scraper._institution_cache.keys()): if keyword in inst_name: info = scraper._institution_cache[inst_name] print(f" ✅ {inst_name} ({info.get('code', '')}) - {info.get('source_file', '')}") print(f"\n共找到匹配结果") elif sys.argv[1] == "--list": # 批量爬取 if len(sys.argv) < 3: print("请提供名称列表文件") sys.exit(1) file_path = sys.argv[2] with open(file_path, 'r', encoding='utf-8') as f: names = [line.strip() for line in f if line.strip()] print(f"\n开始批量爬取 {len(names)} 个机构...") for name in names: result = scraper.scrape_by_name(name) print(format_scraping_result(result)) time.sleep(random.uniform(2, 5)) else: # 单个爬取 name = sys.argv[1] print(f"\n正在爬取: {name}") result = scraper.scrape_by_name(name) print(format_scraping_result(result))