Fliggy Hotel Reviews Scraper (飞猪)

Extract Fliggy (飞猪) hotel guest reviews at scale - by city name or hotel URL - as clean JSON, CSV or Excel, with an LLM-ready markdown column and a ready-made sentiment layer. No login, no account, no official API.

Fliggy is Alibaba's travel platform, and its hotel review corpus is enormous - popular properties carry tens of thousands of guest reviews (Hong Kong Disneyland Hotel alone has 50,000+). This actor turns that into structured data for reputation monitoring, competitor analysis, market research, and AI/RAG pipelines.

⭐ If this actor saves you time, a quick review on the Apify Store genuinely helps - thank you.

What makes this different

• Search by city name, in any language - type Hong Kong, 香港, Beijing or Sanya and get every hotel's reviews. No hotel IDs or city codes to look up.
• Sentiment layer included - each hotel ships ranked aspect-tags with mention counts (服务热心: 680, 交通便利: 572) - a sentiment summary competitors don't provide.
• LLM-ready - every review has a self-contained markdownContent chunk for RAG / fine-tuning.
• Full corpus - deep pagination pulls thousands of reviews per hotel, not just the first page.

What does the Fliggy Hotel Reviews Scraper do?

Pick a mode and it returns:

• Reviews mode - paste Fliggy hotel URLs or just the hotel ID (shid). You get the Reviews dataset (one row per review): 1-5 rating + rating label, per-dimension sub-ratings, review text, check-in date, room type booked, reviewer province and level, photo count, reply threads (optional), aspect tags, and markdownContent. Sort newest-first (default) or by Fliggy's recommended ranking, with an optional from-date cutoff for fast incremental syncs.
• Discovery mode - type city names. It finds every hotel in the city and returns the Hotels dataset (one row per hotel): total review count, positive/negative counts, positive-review %, star rating, address, city, the aggregate 1-5 score, a ready-made sentiment aspect-tag summary, Fliggy's own AI review summary + structured pros/cons, phone, opening year and room count. (Discovery returns the hotel summaries; to pull a hotel's full reviews, feed its URL/ID into Reviews mode.)

It also covers Fliggy travel items (visa / package / ticket / tour) - paste an item URL to get its reviews too.

Does Fliggy have a reviews API?

No public one. Fliggy (飞猪) does not offer an official hotel-reviews API, and its own site requires the app for hotel detail. This actor is the no-code alternative: it returns the same reviews the site shows, as structured JSON / CSV, without login.

	Official Fliggy	This scraper
Public reviews API	❌ none	✅ structured JSON / CSV / Excel
Login / account required	✅ (app)	❌ none
Search by city name	—	✅ any language
Sentiment aspect-tags	—	✅ included
LLM-ready markdown	—	✅ per review

Who is it for?

Hotel chains and revenue managers (reputation + competitor benchmarking), OTAs and travel-tech (review aggregation), market researchers (Chinese-traveler sentiment), and AI teams building travel RAG assistants.

Use cases

Monitor your hotel's reputation on Fliggy

Track ratings, positive/negative counts and fresh reviews on a schedule; alert on new negatives.

Benchmark competitors in a city

Search a city, pull every hotel's aggregate rating + sentiment tags, and compare.

Build a Chinese-hotel review dataset for AI

Export markdownContent straight into a RAG index or fine-tuning set.

Complaint / negative-review analysis

Read each hotel's negative-review count and negative aspect-tags from the Hotels dataset, and sort reviews newest-first to catch fresh complaints as they land.

How to scrape Fliggy hotel reviews (to CSV)

1. Add the actor and open the input form.
2. Leave Mode on Reviews and paste Fliggy hotel URLs or IDs (e.g. https://www.fliggy.com/jiudian/detail/810100/10023497 or just 10022949). To search a whole city instead, switch Mode to Discovery and type city names.
3. Set Max reviews per hotel, Sort (Newest / Recommended), and an optional From date.
4. Click Start, then open the Reviews dataset and Export → CSV (or JSON / Excel).

Input

Field	Description
mode	reviews (scrape the hotels/IDs you paste) or discovery (find hotels in a city).
startUrls	Reviews mode. Fliggy hotel/item URLs, or a bare hotel ID (shid). Every Fliggy URL form is accepted.
searchCities	Discovery mode. City names in any language (Hong Kong, Sanya, Beijing) - discovered automatically.
sortBy	newest (date desc, default) or recommended (Fliggy's ranking).
maxReviews	Cap per hotel (default 200).
fromDate	Only reviews on or after this date. Fastest with newest sort (stops early).
includeReplies	Also fetch reply threads for reviews that have them (makes the run take a bit longer).
maxHotels	Discovery mode only. Hotels to take per city (default 20, max 100).

Output

Reviews (one row per review):

{
  "hotelName": "香港丽豪酒店",
  "reviewId": "514066411",
  "reviewer": { "name": null, "tier": 4, "isAnonymous": true, "ipLocation": "江苏" },
  "overallRating": 5,
  "submittedAt": "2026-08-22 00:35:34",
  "roomName": "高级客房-配备特大双人床",
  "reviewText": "酒店房间比较在香港算比较大的，还带泳池可以游泳...",
  "checkInDate": "2026-07-19",
  "imagesCount": 2,
  "ratingLabel": "非常好",
  "subRatings": ["位置便利: 5", "清洁程度: 5", "服务态度: 5", "设施体验: 5"],
  "tags": [],
  "replies": [],
  "markdownContent": "# 香港丽豪酒店 review\n\n**Rating:** 5/5 ...",
  "extractedAt": "2026-08-31 13:56:14"
}

Hotels (one row per hotel):

{
  "hotelName": "香港丽豪酒店",
  "reviewsCount": 12191,
  "positiveRatePct": 94.2,
  "goodRateCount": 10556,
  "badRateCount": 655,
  "aiSummary": "位置便利，门口公交直达机场（A41），部分房间有河景。早餐多样，自助形式受欢迎……",
  "aiAspects": [{"aspect": "公交站就在门口", "detail": "酒店门口就有公交站，直达机场和市区。", "sentiment": "positive"}],
  "sentimentTags": ["服务热心: 680", "交通便利: 572", "干净卫生: 213"],
  "phone": "+852-26497878",
  "roomCount": "1147间",
  "overallRating": 4.4,
  "hotelStars": 4,
  "cityName": "香港"
}

Pricing

Pay-per-event, three simple parts, with subscription-tier discounts:

Event	Free tier	Discounted (higher plans)
Actor start (per run)	$0.01	down to $0.006
Per review (Reviews mode)	$0.004 ($4 / 1,000)	down to $0.003
Per hotel summary row (Discovery mode)	$0.01	down to $0.006

Reviews mode bills the start fee + reviews. Discovery mode bills the start fee + one hotel summary row per hotel (it does not scrape or bill per-review). A 100-hotel discovery run is about $1; a 5,000-review scrape is about $20. See the Pricing tab for current rates; subscription discounts apply.

Run on a schedule

Use Apify Schedules to run daily or weekly. Combine with fromDate so each run only pulls reviews since the last, keeping cost low and data fresh.

AI agents & RAG

Every review row includes markdownContent - a self-contained markdown chunk (hotel, rating, room, check-in, reviewer location, text). Point your RAG loader at the Reviews dataset and ingest that column directly; no reshaping needed. The actor is also callable from AI agents via the Apify MCP server.

Legal & GDPR

This actor collects publicly displayed review content. Reviewer names are omitted when the review is anonymous, and only the coarse province the platform already shows is included. Use the data in compliance with applicable laws and the platform's terms; you are responsible for your use.

FAQ

Does Fliggy have a reviews API? No public one - this actor is the structured alternative.

Do I need a Fliggy account or login? No.

Do I need a China proxy? No - any proxy works (verified from non-China exits).

What languages are the reviews in? Mostly Chinese, some multilingual; each row keeps the original text.

Can I sort or filter reviews? You can sort newest-first or by Fliggy's recommended ranking, and set a From date cutoff. Fliggy has no server-side rating filter, so for negatives use the Hotels dataset's negative-review count and negative aspect-tags instead.

How many reviews can I get per hotel? Thousands; set maxReviews to control volume and cost.

Can I search without a hotel URL? Yes - type city names under searchCities.

Does it cover flights or prices? No - this actor is hotel & travel-item reviews only.

Can AI agents call it? Yes, via the Apify MCP server.

Is the output CSV/Excel-friendly? Yes - flat rows; export to CSV, Excel, JSON or via API.

Related Actors

• Ctrip & Trip.com Reviews Scraper
• Agoda Hotel Reviews Scraper
• Google Hotels Scraper
• Expedia Hotel Reviews Scraper
• Booking.com Scraper
• All FactDen actors →

Changelog

• 2026-08-31 - v1.0: initial release. Hotel & travel-item reviews, city-name discovery, sentiment aspect-tags, hotel profiles, reply threads, LLM-ready markdown.

Support

• Open the Issues tab on this actor's page (preferred).
• Or email support@factden.com for private / billing / partnership questions.