How to Scrape Fliggy (飞猪) Hotel Reviews (2026 Guide)
Fliggy (飞猪) is Alibaba's travel platform, and its hotels hold one of the largest pools of Chinese-traveler feedback anywhere - popular properties carry tens of thousands of reviews. Almost none of it reaches Western analytics tools. This guide covers what you can extract, why building your own scraper is harder than it looks, and the fastest way to get clean, structured Fliggy review data.
Why scrape Fliggy reviews?
- Reputation monitoring - track your hotel's Fliggy rating and fresh reviews over time.
- Competitor benchmarking - pull every hotel in a city and compare ratings and sentiment.
- Chinese-traveler intelligence - understand what mainland guests praise and complain about.
- AI & RAG - build a review dataset for sentiment models and travel assistants.
What data can you get?
Per review: a 1-5 rating, the original review text, the guest's check-in date and room type, the reviewer's province and level, photo count, reply threads, aspect tags, and an LLM-ready markdownContent block. Per hotel: total review count, positive and negative counts and positive-review %, a ready-made sentiment aspect-tag summary with mention counts (like 服务热心: 680), Fliggy's own AI review summary plus structured pros/cons, phone, opening year and room count. Star rating, address, city and the aggregate 1-5 score come with every discovery result.
Why building your own breaks
Fliggy changes its page layouts and pagination often, pagination can silently cap, and hotel details, reviews and aspect tags have to be stitched together into one clean structure. Keeping a scraper working is ongoing maintenance, and most teams spend weeks and still miss much of the review corpus.
The fast way: the FactDen Fliggy Hotel Reviews Scraper
The Fliggy Hotel Reviews Scraper on Apify does it in one run. You can:
- Search by city name - type
Hong Kong,香港,BeijingorSanya; it finds every hotel and pulls its reviews. No IDs or city codes. - Or paste hotel URLs - like
https://www.fliggy.com/jiudian/detail/810100/10023497, or just the hotel ID. - Sort and filter - newest-first or Fliggy's recommended ranking, a from-date cutoff for incremental syncs, optional reply threads, and a per-hotel review cap.
- Export to JSON, CSV, Excel, or pull from the API on a schedule.
No login, no API key, and no setup. Field docs and code snippets are in the GitHub repo.
What does it cost?
$4 per 1,000 reviews plus a $0.01 per-run start fee and $0.01 per hotel summary row, pay-per-result. New Apify accounts get free platform credit to try it end to end.
Frequently asked questions
- Does Fliggy have a reviews API?
- No public one. This scraper is the structured alternative - JSON or CSV, no login.
- Can I scrape by city instead of by URL?
- Yes. Type city names in any language and it discovers every hotel automatically.
- Are the reviews translated?
- Fliggy reviews are mostly Chinese; the scraper returns the original text as shown.
Try the Fliggy Hotel Reviews Scraper →
Related guides: How to scrape Trip.com & Ctrip reviews →