How to Scrape Trip.com and Ctrip Hotel Reviews in 2026 (Python + a No-Code Shortcut)

Most "Trip.com scrapers" only see half the picture. too.

What you can pull from a review

Per review you can get:

• 
  Overall rating and a label ("Outstanding"), plus sub-ratings: Cleanliness, Location, Service, Facilities


• 
  The review text, plus an English translation of Chinese reviews (reviewTextTranslated), and the detected language
  


• 
  Trip context: travel type (family, couple, business, solo), room name, check-in month


• 
  Reviewer detail: tier ("Review star"), lifetime review count, and for Ctrip the reviewer's Chinese province


• 
  Owner responses (text and date), useful counts, photo/video flags


• 
  An LLM-ready markdown block per review

The two things that separate a real dataset from a shallow one: the Chinese-domestic feed and its translation.

Why scraping Trip.com and Ctrip is hard

1. 
   There is no public reviews API. Trip.com Group does not offer one, so scraping the public pages is the only route.


2. 
   It is two systems, not one. trip.com (international) and hotels.ctrip.com / 携程 (Chinese) have different structures and languages. A scraper built for one usually misses the other, and the Chinese pool is often the larger one.


3. 
   Chinese reviews need translation to be usable in an English pipeline, which is a whole extra step.


4. 
   Bot protection and rate limits apply on both.

So the work is not parsing one page. It is handling both locales, translating, and staying unblocked, on repeat.

Three ways to get the data

Option A: DIY in Python

A plain request to a hotel page tends to come back blocked or JavaScript-only:

import httpx



url = "https://www.trip.com/hotels/macau-hotel-detail-344983/galaxy-hotel/"

r = httpx.get(url, headers={"User-Agent": "Mozilla/5.0"})

print(r.status_code)   # challenge / JS-rendered shell, reviews not in the HTML

Reviews load through internal endpoints, so you end up reverse-engineering those (per locale), then normalizing two different response shapes, then translating the Chinese text. Doable for one hotel, painful for a portfolio on a schedule.

Option B: the no-code / API shortcut

When you want clean rows, the .

Grab a free sample dataset

Want to see the data first? There is a free Trip.com/Ctrip sample (CSV/JSON) here: .