Building a Xiaohongshu (RedNote) E-commerce Scraper for RedShop Product Data

When Xiaohongshu (RedNote / Little Red Book / 小红书) launched RedShop — its US-facing e-commerce platform — in April 2026, I noticed every existing scraper on Apify only covered the social side: posts, profiles, comments, videos. None of them touched product listings, vendor catalogs, or pricing data.

So I built one.

Why a dedicated shop scraper?

Xiaohongshu is unusual among Chinese platforms because product listings live in a separate URL space from social posts. The all-in-one social scrapers handle the /explore/ posts surface. RedShop products live behind /goods-detail/ with completely different structure.

Trying to extract product data from a "social" scraper means hacky workarounds. A dedicated commerce-focused tool gives you:

• Structured product fields (price, sold count, SKU variants, vendor metadata)
• Native support for vendor/store browsing
• Cross-border vs domestic flagging
• Cleaner pricing model: charge per product, not per "result"

What it extracts

For each product:

• itemId, title, productUrl
• salePrice, originalPrice, discountPct, currency (CNY for domestic, USD for cross-border)
• soldCount, wantCount (popularity signals)
• cover, images
• vendor (sellerId, name, rating)
• category path
• skus (variants with prices and stock)
• crossBorder flag and shippingOrigin

Three modes

Real-world use cases

• DTC brands: monitor your own listings and competitor pricing on China's #1 social commerce platform
• Dropshippers and resellers: discover trending Chinese products before they hit Amazon or Etsy
• Cross-border arbitrage: identify SKUs popular in China that haven't reached Western markets yet
• Investment analysts: track e-commerce activity for Chinese consumer brands
• Sourcing agents: scout Chinese products at scale for clients in cosmetics, fashion, or home goods

Combined with the RedNote All-in-One Scraper (social side), you can map products to the influencers tagging them — extremely valuable for influencer-product correlation studies.

How to use it

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("zhorex/rednote-shop-scraper").call(run_input={
    "mode": "product_search",
    "searchQuery": "skincare",
    "maxResults": 100,
    "sortBy": "sales",
    "minPrice": 50,
    "maxPrice": 500,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['title']} — ¥{item['salePrice']} (sold {item['soldCount']})")

Output sample

{
    "itemId": "642a1b3c0000000023019f7e",
    "title": "Skincare Set - Hydrating Toner + Serum + Moisturizer",
    "salePrice": 199.00,
    "originalPrice": 299.00,
    "discountPct": 33.4,
    "currency": "CNY",
    "soldCount": 12500,
    "wantCount": 5400,
    "vendor": {"sellerId": "...", "name": "BeautyBrand Official", "rating": 4.8},
    "category": "Beauty / Skincare / Sets",
    "skus": [{"spec": "Normal Skin", "price": 199.00, "stock": 1200}],
    "crossBorder": false,
    "shippingOrigin": "China"
}

Pricing

Pay-per-event:

• $0.0075 per product scraped
• $0.025 per vendor info record

Typical costs:

• Search 100 products: ~$0.75
• Full vendor catalog (200 products): ~$1.53
• 5 competitor vendors with 100 products each: ~$3.88

FAQ

Does it work for cross-border products?
Yes — products are explicitly flagged in the output (crossBorder: true/false) so you can filter domestic vs international listings.

Can I track price changes over time?
Schedule the actor to run daily/weekly via Apify Schedules. The dataset versioning gives you a price history for any product or vendor.

Does it need a proxy?
Residential proxies are recommended for reliable results. The default config uses Apify's residential pool.

Is there an official Xiaohongshu shop API?
No — Xiaohongshu doesn't offer a commerce API for international developers. This actor is the practical alternative.

Try it

RedNote Shop Scraper on Apify — works with Apify's free plan ($5/month credits cover hundreds of products at no cost).

If you build something useful with it, drop a comment — always interested in seeing how people use commerce data.