Trip.com Hotels, Rooms & Reviews Scraper

What Trip.com Hotels, Rooms & Reviews Scraper can do

• Real destination search. Type "Tokyo" or "New York" - no city ids to hunt for. Trip.com city ids and id|Name strings still work for power users.
• Every Trip.com link works in URL mode. Hotel list links, hotel detail links, and the SEO links search engines show (like /hotels/tokyo-hotels-list-228/) all resolve - missing dates and guests are filled from your input automatically.
• Full hotel record in one run. Price (current, original, with taxes and fees), star rating, guest review score, images, amenities, policies, nearby places and a description, all in a single dataset row per hotel.
• Room level detail and the full guest review feed, not just an average. Rooms nest per hotel with cancellation terms and per room pricing; reviews carry per category ratings, traveler type and useful count, plus an aggregate breakdown and an AI generated summary. Cap the feed with Max reviews (default 20).
• Filters that don't starve later pages. Minimum star rating and minimum guest review score are applied before any per page item cap, and each destination keeps being resampled until it stops producing hotels you haven't already collected.
• Incremental mode for schedules. Let the actor remember what it already delivered and push only what is new or changed since the last run, with changeType on every row.

Choose what to collect

These inputs let you configure Trip.com Hotels, Rooms & Reviews Scraper. Open the actor on Apify to enter your values and review the current options.

Fields in the output

The documented dataset includes the fields below. Availability can depend on the source page and selected mode.

Example output

An example from this actor’s documentation. Values are illustrative; this is not a fresh live result.

{
  "hotelId": "0000000",
  "name": "Sample Grand Hotel",
  "starRating": 4,
  "reviewScore": 9.1,
  "reviewScoreLabel": "Great",
  "reviewCount": 1200,
  "reviewTags": [
    "Easy to get around",
    "Delicious breakfast"
  ],
  "address": "Sample District, Sample City",
  "lat": 35,
  "lng": 139,
  "priceCurrent": 120,
  "priceOriginal": 140,
  "priceTotalWithTaxes": 134,
  "discountPercent": 14,
  "discountLabel": "14% off",
  "currency": "USD",
  "images": [
    "https://example.com/photo1.jpg"
  ],
  "image": "https://example.com/photo1.jpg",
  "badges": [
    "Free Cancellation"
  ],
  "medal": null,
  "propertyTag": "Hotel",
  "detailUrl": "https://www.trip.com/hotels/detail/?hotelId=0000000&checkIn=2026-11-15&checkOut=2026-11-16&adult=2&children=0&curr=USD",
  "amenities": [
    "Free WiFi",
    "Fitness center",
    "Parking"
  ],
  "policies": {
    "checkin": "After 15:00",
    "checkout": "Before 11:00",
    "cancellation": "Free cancellation"
  },
  "nearbyPlaces": [
    {
      "name": "Sample Station",
      "type": "station",
      "distance": "400m"
    }
  ],
  "contactPhone": "+1-000-0000000",
  "contactEmail": "stay@samplegrandhotel.com",
  "rooms": [
    {
      "roomId": "0",
      "roomName": "Superior Queen Room",
      "roomPrice": {
        "current": 120,
        "original": 140,
        "currency": "USD",
        "taxesAndFeesTotal": 14
      }
    }
  ],
  "roomCount": 1,
  "reviews": [
    {
      "reviewId": "0",
      "reviewerNickname": "Jane Doe",
      "reviewRatingOverall": 9,
      "reviewContent": "Great location and clean room."
    }
  ],
  "reviewCount_pushed": 20,
  "aggregateTotalReviewCount": 1200,
  "aggregateRatingBreakdown": {
    "overall": 9.1,
    "location": 9.3,
    "facility": 8.9,
    "service": 9,
    "room": 9
  },
  "aggregateCommentTags": [
    "Clean and tidy",
    "Great location"
  ],
  "aggregateAiSummary": "Facilities: guests liked the range of amenities. Guest Suggestion: some guests wanted a quicker check-in process.",
  "cityId": 228,
  "cityName": "Tokyo",
  "scrapedAt": "2026-11-01T00:00:00.000Z"
}

Ways to use this data

• Travel agencies and OTAs: benchmark Trip.com's pricing and availability against your own listings for a destination and date range.
• Rate and demand analysts: track price, discount and occupancy signals across many hotels in a city over time - incremental mode makes the scheduled re-runs cheap to read.
• Reputation and review teams: pull the full guest review feed and AI summary to spot recurring complaints or praised amenities.
• Travel content and comparison sites: build destination pages with live pricing, star ratings and photos.
• Revenue managers: compare your own property's room types and cancellation terms against nearby competitors listed on Trip.com.

Before you run

Start with a small input and check the resulting records against your expected fields. Source content and actor options can change; the current Apify listing is the reference for availability and pricing.

Questions about this actor