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当用户需要获取番茄小说榜单数据（新书榜、阅读榜）时使用此技能 - 分析番茄小说各分类竞争程度、获取热门书籍信息（书名、作者、简介、在读数、字数）、了解当前市场趋势和热门题材
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Based on SOC occupation classification
| name | fanqie-rank |
| description | 当用户需要获取番茄小说榜单数据（新书榜、阅读榜）时使用此技能 - 分析番茄小说各分类竞争程度、获取热门书籍信息（书名、作者、简介、在读数、字数）、了解当前市场趋势和热门题材 |
| allowed-tools | Read, Bash |
当用户需要获取番茄小说榜单数据（新书榜、阅读榜）时使用此技能。
番茄榜单、新书榜、阅读榜、榜单数据、抓取榜单、fanqie rank
https://fanqienovel.com/rank/{gender}_{type}_{category_id}
参数说明：
gender: 1=男频, 0=女频type: 1=新书榜, 2=阅读榜category_id: 分类ID| ID | 分类名 | ID | 分类名 |
|---|---|---|---|
| 1141 | 西方奇幻 | 1140 | 东方仙侠 |
| 8 | 科幻末世 | 261 | 都市日常 |
| 124 | 都市修真 | 1014 | 都市高武 |
| 273 | 历史古代 | 27 | 战神赘婿 |
| 263 | 都市种田 | 258 | 传统玄幻 |
| 272 | 历史脑洞 | 539 | 悬疑脑洞 |
| 262 | 都市脑洞 | 257 | 玄幻脑洞 |
| 751 | 悬疑灵异 | 504 | 抗战谍战 |
| 746 | 游戏体育 | 718 | 动漫衍生 |
| 1016 | 男频衍生 |
| ID | 分类名 | ID | 分类名 |
|---|---|---|---|
| 1139 | 古风世情 | 8 | 科幻末世 |
| 746 | 游戏体育 | 1015 | 女频衍生 |
| 248 | 玄幻言情 | 23 | 种田 |
| 79 | 年代 | 267 | 现言脑洞 |
| 246 | 宫斗宅斗 | 539 | 悬疑脑洞 |
| 253 | 古言脑洞 | 24 | 快穿 |
| 749 | 青春甜宠 | 745 | 星光璀璨 |
| 747 | 女频悬疑 | 750 | 职场婚恋 |
| 748 | 豪门总裁 | 1017 | 民国言情 |
根据榜首在读数判断：
番茄小说榜单页面使用字体加密，直接提取会得到乱码。
解决方案: 通过书籍详情页 /page/{bookId} 获取真实书名，该页面不使用字体加密。
页面数据存储在 window.__INITIAL_STATE__ JSON中：
rank.book_list: 书籍列表bookId, bookName(加密), author(加密), abstract(加密), read_count, wordNumber#!/usr/bin/env python3
import json
import re
import urllib.request
import time
from pathlib import Path
# 分类映射
MALE_CATEGORIES = {
"1141": "西方奇幻", "1140": "东方仙侠", "8": "科幻末世",
"261": "都市日常", "124": "都市修真", "1014": "都市高武",
"273": "历史古代", "27": "战神赘婿", "263": "都市种田",
"258": "传统玄幻", "272": "历史脑洞", "539": "悬疑脑洞",
"262": "都市脑洞", "257": "玄幻脑洞", "751": "悬疑灵异",
"504": "抗战谍战", "746": "游戏体育", "718": "动漫衍生",
"1016": "男频衍生"
}
FEMALE_CATEGORIES = {
"1139": "古风世情", "8": "科幻末世", "746": "游戏体育",
"1015": "女频衍生", "248": "玄幻言情", "23": "种田",
"79": "年代", "267": "现言脑洞", "246": "宫斗宅斗",
"539": "悬疑脑洞", "253": "古言脑洞", "24": "快穿",
"749": "青春甜宠", "745": "星光璀璨", "747": "女频悬疑",
"750": "职场婚恋", "748": "豪门总裁", "1017": "民国言情"
}
def fetch_rank_page(gender, rank_type, category_id):
"""获取榜单页面HTML"""
url = f"https://fanqienovel.com/rank/{gender}_{rank_type}_{category_id}"
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)'}
req = urllib.request.Request(url, headers=headers)
with urllib.request.urlopen(req, timeout=30) as resp:
return resp.read().decode('utf-8')
def parse_initial_state(html):
"""从HTML提取__INITIAL_STATE__数据"""
match = re.search(r'window\.__INITIAL_STATE__\s*=\s*({.*?});', html, re.DOTALL)
if match:
return json.loads(match.group(1))
return None
def get_real_book_info(book_id):
"""从书籍详情页获取真实信息（绕过字体加密）"""
url = f"https://fanqienovel.com/page/{book_id}"
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)'}
req = urllib.request.Request(url, headers=headers)
try:
with urllib.request.urlopen(req, timeout=10) as resp:
content = resp.read().decode('utf-8')
book_name = re.search(r'"bookName":"([^"]*)"', content)
author = re.search(r'"author":"([^"]*)"', content)
abstract = re.search(r'"abstract":"([^"]*)"', content)
return {
'bookName': book_name.group(1) if book_name else None,
'author': author.group(1) if author else None,
'abstract': abstract.group(1) if abstract else None
}
except Exception as e:
print(f"获取书籍 {book_id} 失败: {e}")
return None
def get_competition_level(top_read):
"""根据榜首在读数判断竞争程度"""
read_wan = top_read / 10000
if read_wan > 50:
return "🔴🔴 超红海"
elif read_wan > 30:
return "🔴 红海"
elif read_wan > 25:
return "🟡 中等偏高"
elif read_wan > 20:
return "🟡 中等"
else:
return "🟢 蓝海"
def scrape_category(gender, rank_type, category_id, category_name):
"""抓取单个分类的榜单数据"""
print(f"正在抓取: {category_name}")
html = fetch_rank_page(gender, rank_type, category_id)
data = parse_initial_state(html)
if not data or 'rank' not in data:
return None
book_list = data['rank'].get('book_list', [])[:10]
results = []
for i, book in enumerate(book_list, 1):
book_id = book.get('bookId', '')
read_count = int(book.get('read_count', 0))
word_count = int(book.get('wordNumber', 0))
# 获取真实书籍信息
real_info = get_real_book_info(book_id)
time.sleep(0.3) # 避免请求过快
if real_info:
results.append({
'rank': i,
'name': real_info['bookName'],
'author': real_info['author'],
'abstract': real_info['abstract'],
'read': read_count / 10000, # 转换为万
'words': word_count / 10000
})
return results
def generate_markdown(all_data, title="番茄小说榜单数据"):
"""生成Markdown格式报告"""
lines = [f"# {title}", "", f"> 数据采集时间：{time.strftime('%Y-%m-%d')}", ""]
# 按竞争程度排序
sorted_cats = sorted(all_data.items(),
key=lambda x: x[1]['top_read'], reverse=True)
for cat_name, cat_data in sorted_cats:
level = cat_data['level']
top_read = cat_data['top_read']
lines.append(f"## {cat_name}（榜首{top_read:.1f}万在读）{level}")
lines.append("")
for book in cat_data['books']:
lines.append(f"**【{book['rank']}】《{book['name']}》**")
lines.append(f"- 作者：{book['author']}")
lines.append(f"- 在读：{book['read']:.1f}万 | 字数：{book['words']:.1f}万")
if book['abstract']:
abstract = book['abstract'][:100] + "..." if len(book['abstract']) > 100 else book['abstract']
lines.append(f"- 简介：{abstract}")
lines.append("")
lines.append("---")
lines.append("")
return "\n".join(lines)
# 主函数示例
def main():
gender = 1 # 1=男频, 0=女频
rank_type = 1 # 1=新书榜, 2=阅读榜
categories = MALE_CATEGORIES if gender == 1 else FEMALE_CATEGORIES
all_data = {}
for cat_id, cat_name in categories.items():
books = scrape_category(gender, rank_type, cat_id, cat_name)
if books:
top_read = books[0]['read'] if books else 0
all_data[cat_name] = {
'level': get_competition_level(top_read * 10000),
'books': books,
'top_read': top_read
}
time.sleep(1) # 分类间隔
# 生成报告
md = generate_markdown(all_data, "番茄小说男频新书榜 TOP10")
# 保存文件
output_path = Path("fanqie-newbook-data.md")
output_path.write_text(md, encoding='utf-8')
print(f"数据已保存到: {output_path}")
if __name__ == "__main__":
main()
gender 和 rank_type 变量fanqie-newbook-data.md最新抓取的完整数据保存在：
novel-writer-skills/templates/knowledge-base/genres/fanqie-male-newbook-top10.mdnovel-writer-skills/templates/knowledge-base/genres/fanqie-trends-2025.md