Qunar Hotel Reviews Scraper - 去哪儿 Ratings, Reviews & Sub-scores (August 2026)

Qunar Hotel Reviews Scraper extracts guest reviews from Qunar (去哪儿), the China-market travel brand in the Trip.com Group, into clean JSON, CSV or Excel. It is the only Qunar review scraper on Apify: paste a hotel URL and get guest ratings, per-review sub-scores, review text, travel type, photos, owner replies, sentiment and reviewer data, each with an LLM-ready markdown field. No login, no API key, no code required.

⭐ Found this useful? Please rate and bookmark the actor. It helps other China-market researchers find it.

From $4 per 1,000 reviews (dropping to $3 with volume) plus a small per-run start fee. New Apify accounts get free platform credit, enough to validate the scraper end to end before any real spend.

Contents: What's different · What data can I extract? · Why scrape Qunar reviews? · Step by step · Input · Output · Pricing · Qunar vs other scrapers · Schedule · AI & RAG · GDPR · FAQ · Related actors · Changelog · Support

---

What's different

Qunar runs one of the largest Chinese-language hotel review systems, and no other Apify actor covers it. This scraper is built for it, so you get review data you cannot get from Trip.com, Ctrip or Western OTA scrapers:

• Everything the Qunar page shows. Qunar displays reviews written natively on Qunar plus reviews it aggregates from Ctrip, undifferentiated. You get them all, and every row is tagged with its origin (reviewSource, isCtripImport) so you always know where each review came from.
• Per-review sub-ratings. Native reviews carry service, location, facilities, cleanliness and breakfast scores per review, not just a hotel-level average. Most OTA scrapers only give the overall star.
• Domestic-China coverage. Qunar's audience is mainland domestic travelers, the corpus that Trip.com's international listings and Western OTAs largely miss.
• LLM-ready markdown. Each review carries a self-contained markdownContent block, ready to embed straight into a vector database. No other hotel-review actor ships this.
• A paired Hotels dataset with the hotel's overall score, recommend rate and sub-scores, one summary row per hotel.

No-setup checklist: no login or account, Apify Proxy bundled (datacenter by default), form-based input in the Apify Console.

What data can I extract?

Field group	Fields
Identity	reviewId, hotelId, hotelName, hotelCity, hotelUrl, url, source, reviewSource, isCtripImport
Timing	submittedAt, checkInMonth
Ratings	overallRating (1-5), ratingLabel, subRatings, sentiment (POSITIVE / NEGATIVE / NEUTRAL)
Content	reviewTitle, reviewText, travelType, roomName, language
Media	imagesCount, images
People	reviewer (name, userId, avatar, isAnonymous, identityLevel), ownerResponse
AI	markdownContent (LLM-ready)

Ratings are on Qunar's native 1 to 5 scale, matching the website. Reviews are Chinese-language and are returned as written (Qunar provides no translation).

Why scrape Qunar reviews?

• Reputation and revenue teams tracking guest sentiment on China's price-led OTA, the domestic market Western tools do not cover.
• Market researchers comparing Qunar reception against Ctrip, Trip.com and the wider field.
• AI and RAG builders who need clean, per-review Chinese-language markdown for question-answering.
• Data teams feeding review pipelines, dashboards or sentiment models.

How to scrape Qunar reviews, step by step

1. Open the input form.
2. Paste one or more Qunar hotel URLs into Qunar hotel URLs, for example https://hotel.qunar.com/cn/nanjing/dt-17/ or https://touch.qunar.com/hotelcn/nanjing/dt-17. You can also add composite IDs like nanjing_17 under Hotel IDs.
3. Optionally set maxReviews per hotel, or a fromDate cutoff for incremental syncs.
4. Click Start. Download results as JSON, CSV or Excel, or pull them from the API.

Input

Field	Type	Description
startUrls	array	Qunar hotel page URLs. Combined with hotelIds, up to 100 hotels per run.
hotelIds	array	Optional composite IDs in {city}_{id} form, e.g. nanjing_17. A bare number is rejected (it has no city).
maxReviews	integer	Max reviews per hotel, newest first. Qunar caps accessible reviews at 10,000. Default 200.
fromDate	string	Keep reviews submitted on or after this YYYY-MM-DD date. Reviews are read newest-first, so this stops the crawl at the date boundary (fast incremental syncs) instead of scanning the whole history.
proxyConfiguration	object	Apify Proxy. Datacenter by default.

Rating filtering is done downstream, not in the input: Qunar's endpoint offers no server-side rating sort, so every row carries overallRating (1-5) and sentiment for you to sort or filter after export.

Example input

{
  "startUrls": ["https://hotel.qunar.com/cn/nanjing/dt-17/"],
  "maxReviews": 50
}

Output

Two datasets: Reviews (one row per review, with markdownContent) and Hotels (one summary row per hotel). Timestamps (submittedAt) are in Qunar's own China local time (UTC+8), matching qunar.com, and the fromDate filter keys on that local date. (The Apify Console table converts these naive times to UTC for display, so an early-morning review can show on the previous day there; the stored value is authoritative.)

Sample review row

{
  "reviewId": "2856615368",
  "hotelId": 17,
  "hotelName": "南京维景国际酒店",
  "hotelCity": "nanjing",
  "url": "http://review.qunar.com/h/nanjing_17/2856615368",
  "source": "qunar",
  "reviewSource": "qunar_iphone",
  "isCtripImport": false,
  "submittedAt": "2026-08-26T21:55:09",
  "checkInMonth": "2026-08",
  "reviewer": { "name": "p***2", "userId": "12345", "avatar": "https://img1.qunarzz.com/...", "isAnonymous": false, "identityLevel": 0 },
  "travelType": "家庭出行",
  "roomName": "四人家庭房",
  "overallRating": 5,
  "ratingLabel": "非常好",
  "subRatings": ["Service: 5", "Location: 5", "Facilities: 5", "Breakfast: 5"],
  "reviewTitle": "前台服务好,大厅宽敞",
  "reviewText": "前台小吴服务好，大厅宽敞...",
  "sentiment": "POSITIVE",
  "imagesCount": 2,
  "images": ["202608/26/AbC...", "202608/26/DeF..."],
  "ownerResponse": null,
  "markdownContent": "# 南京维景国际酒店 review (Qunar)\n\n**Rating:** 5/5 ...",
  "extractedAt": "2026-08-26T15:08:35"
}

Pricing

Pay per result, on Apify's pay-per-event model. Both events carry automatic volume discounts that kick in as your Apify plan scales:

Event	Free / Bronze	Silver	Gold and above
Review (per review row)	$0.004	$0.0035	$0.003
Actor start (once per run)	$0.01	$0.008	$0.006

Effective rate: $4 per 1,000 reviews, dropping to $3 per 1,000 at higher tiers. Reviews removed by your fromDate filter are not charged.

Qunar vs other review scrapers

There is no other Qunar scraper on Apify, so the honest comparison is against what general OTA and review scrapers can and cannot reach:

Capability	This actor	Trip.com / Ctrip scrapers	Generic review scrapers
Qunar (去哪儿) native reviews	Yes	No	No
Per-review sub-ratings	Yes	Hotel-level only	Rarely
Domestic-China corpus	Yes	Partial	No
Owner responses + sentiment	Yes	Sometimes	Sometimes
LLM-ready markdown field	Yes	No	No
Paired hotel-summary dataset	Yes	Sometimes	No

Run on a schedule

Use Apify Schedules to run this actor daily or weekly. Combine a schedule with fromDate set to your last sync date so each run returns only new reviews, keeping incremental costs low.

AI agents & RAG

Every review row carries a self-contained markdownContent block (rating, sub-scores, travel type, body and the hotel's reply), and the AI ingest dataset view puts it first, so you can push results straight into a vector store with no reformatting.

Does it work with MCP and AI agents? Yes. The actor runs through the Apify API and the Apify MCP server, so an AI agent can call it with a hotel URL and get structured reviews back.

Data & GDPR

This actor collects publicly displayed hotel reviews. Reviewer names on Qunar are already masked or pseudonymous and are passed through as shown; you are responsible for using the data in line with applicable laws (including GDPR where relevant) and Qunar's terms. Reviews are Chinese-language; the source does not provide translations.

FAQ

Is it legal to scrape Qunar reviews? Scraping publicly available data is generally lawful, but how you use it is regulated. Reviews here are public and reviewer names are already pseudonymized. Use the data in line with GDPR and Qunar's terms. See Apify's guide: https://blog.apify.com/is-web-scraping-legal/

Does Qunar have a public reviews API? No. Qunar offers no supported reviews API, and the reviews are not in the page HTML (they load from a separate guarded call). This actor is the no-code way to get them as structured data.

Is Qunar the same as Ctrip or Trip.com? Qunar (去哪儿) is a separate brand in the same Trip.com Group. It is the price-led, domestic-China site, and it shows both native Qunar reviews and reviews aggregated from Ctrip. This actor returns both, tagged by origin.

Can I use this actor with the Apify API? Yes. Start runs and pull the dataset from the Apify API in any language, or use the JS/Python Apify client. Every run writes a Reviews dataset and a Hotels dataset.

Can I use it through an MCP server? Yes. The actor is callable from the Apify MCP server, so Claude, ChatGPT and other MCP-aware agents can run it with a hotel URL and receive structured review JSON, including the LLM-ready markdownContent field.

Can I integrate it with Make, Zapier, n8n or Google Sheets? Yes. Use Apify's integrations or webhooks to push new reviews into Make, Zapier, n8n, Google Sheets, Slack or your database on every run.

How do I scrape only new Qunar reviews by date? Set fromDate to your last sync date. Because reviews are read newest-first, the actor stops at that boundary instead of scanning the whole history, so daily syncs stay cheap.

Are the reviews translated? No. Qunar serves Chinese-language reviews and provides no machine translation, so reviewText is the original Chinese. Run it through your own translation step if you need English.

Related actors by FactDen

• Trip.com & Ctrip Reviews Scraper
• Agoda Hotel Reviews Scraper
• Google Hotels Scraper
• Expedia Hotel Reviews Scraper
• Hotels.com Reviews Scraper
• MakeMyTrip Scraper
• Airbnb Data Scraper

Support

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

Changelog

• 2026-08 (v1.0.4): Tiered volume pricing (Review $4 to $3 per 1,000; start fee tiered). Curated Overview dataset view; documented submittedAt as Qunar China local time.
• 2026-08 (v1.0): Initial release. Native + Ctrip-imported Qunar reviews, per-review sub-ratings, per-review permalink, sentiment, photos, owner replies, hotel summary dataset, and LLM-ready markdown.