[DISCONTINUED] JD.com Product Scraper
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[DISCONTINUED] JD.com Product Scraper
Deprecated[DISCONTINUED 2026-05-22] JD.com's anti-bot stack blocks all Apify proxy paths. Runs exit without pushing data or charging. See Alibaba Product Scraper or Eastmoney China Stock Screener for working alternatives.
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⚠️ [DISCONTINUED] JD.com Product Scraper
This actor has been discontinued as of 2026-05-22. JD.com's anti-bot stack (passport.jd.com redirect + browser fingerprinting + the "大促异常火爆" deflection error) reliably blocks all Apify proxy paths — direct egress, datacenter proxies, and residential proxies from CN/HK/TW/SG, both raw-HTTP and Playwright-stealth. Multiple rebuild attempts (versions 0.1.1 through 0.1.12) all ended on the same bounce. Same outcome as our Glassdoor, G2, and Healthgrades anti-bot kill list.
Runs of this actor now exit cleanly without pushing data and without charging any events. Existing paying users will incur no further charges.
Alternatives:
- Alibaba Product Scraper — global cross-border B2B/B2C sourcing
- Eastmoney Chinese A-Share Stock Screener — China financial data (working alternative for Chinese-market intelligence)
- Bilibili Video Search and Weibo / Baidu / Toutiao Trends — Chinese content/social signal data
Scrape JD.com (京东) — China's #2 e-commerce platform — for product pricing, brand data, seller information, reviews, ratings, and JD自营 (self-operated) vs 第三方 (third-party) classification. Built for cross-border e-commerce sellers, supply-chain analysts, brand monitoring teams, and Chinese-market intelligence operators who need clean, structured product data without spinning up their own Chinese-internet scraping stack.
JD.com is the workhorse of Chinese e-commerce for electronics, appliances, FMCG, and premium goods. Where Taobao/Tmall is the long tail, JD is curated, fast-shipping, and largely first-party — which makes its pricing, stock signals, and review data unusually high-signal for sourcing and competitive intelligence. This actor turns search.jd.com and item.jd.com into a structured product dataset you can ingest into BigQuery, Snowflake, Postgres, an internal dashboard, or a Claude/GPT-powered analyst workflow with one HTTP call.
Who this is for
- Cross-border e-commerce operators sourcing products in China to resell on Amazon, Shopify, eBay, TikTok Shop, or Temu — and who need real-time JD pricing as a price-floor reference.
- Supply-chain analysts tracking JD自营 first-party listings as a leading indicator of category demand and inventory health.
- Brand monitoring & MAP enforcement teams watching how their brand appears on JD — unauthorized resellers, gray-market listings, MAP violations, and counterfeits.
- Chinese-market intelligence consultancies producing competitive landscape reports on consumer categories (mobile, appliances, beauty, FMCG, maternity, fresh).
- Investors and equity researchers tracking JD-listed brand performance, discount intensity, and review velocity as alt-data for JD.com (NASDAQ: JD) and competitor coverage.
- AI agents and LLM pipelines that need a clean
tool_callagainst JD product data without manually managing a Playwright cluster behind the Great Firewall.
What you get
For every product matched, the actor returns a structured record:
| Field | Type | Description |
|---|---|---|
sku_id | string | JD product ID — stable, used in item.jd.com/{sku_id}.html |
name | string | Full product title (Chinese, occasionally English) |
brand | string | Brand parsed from the product spec sheet |
category | string | Breadcrumb path (e.g. 家用电器 > 大家电 > 平板电视) |
current_price_cny | float | Live price in CNY (¥) |
original_price_cny | float | Pre-discount / MSRP price in CNY |
discount_pct | float | Computed discount percentage |
seller_type | string | JD自营 (self-operated) or 第三方 (marketplace) |
seller_name | string | Shop / merchant name |
rating_avg | float | Average product rating (1–5 stars) |
review_count | int | Total review count |
is_jd_logistics | boolean | Whether 京东物流 (JD Logistics) ships the SKU |
image_url | string | Primary product image |
product_url | string | Canonical item.jd.com/{id}.html link |
data_source | string | Always jd.com for live records |
Input filters
| Field | Default | Notes |
|---|---|---|
keywords | ["手机"] | Array of search terms. Chinese keywords work best. |
category | "" | Optional JD category ID (e.g. 9987,653,655). |
min_price | 0 | Minimum CNY price filter. |
max_price | 0 | Maximum CNY price filter. |
sort | default | default / sales (销量) / price (low→high). |
limit | 30 | Total products across all keywords. |
Example input:
{"keywords": ["空气炸锅", "扫地机器人"],"min_price": 200,"max_price": 2000,"sort": "sales","limit": 100}
Pricing
$0.01 per run (platform overhead) + $0.05 per product record.
- 30 products ≈ $1.51
- 100 products ≈ $5.01
- 1,000 products ≈ $50.01
This is volume-friendly e-commerce pricing — designed for sourcing teams running daily category sweeps and brand-monitoring teams running rolling 7-day SKU panels. You only pay for products actually returned; if JD blocks a query, you'll see a data_source: jd.com:maintenance stub record (one per failed keyword) and no overcharge.
How it works under the hood
JD.com has a non-trivial anti-bot layer on plain-HTTP search XHRs. The actor uses a stealthed headless Chromium (Playwright) to fetch search.jd.com/Search pages, scrolls to trigger lazy-loaded cards, parses out SKU IDs and card-level pricing, and then enriches each SKU via the public item.jd.com/{sku_id}.html endpoint (which serves real HTML to plain HTTP clients) and the JD comment summary API for ratings.
Concurrent enrichment uses a 4-way semaphore so you won't hammer JD into rate-limiting you. If JD hard-blocks a keyword, the actor returns a maintenance stub instead of crashing — your downstream pipeline will see the failure clearly without 5xx alerts.
Comparison vs alternatives
| Product | JD.com Product Scraper | Alibaba 1688 | Shopify Markets | Sensor Tower | Sinotrust |
|---|---|---|---|---|---|
| Focus | JD.com retail SKUs, real consumer-facing prices | Wholesale / B2B 1688 listings | Your own Shopify storefronts in CN region | Mobile app intelligence | Premium consulting reports |
| Real-time? | Yes — live JD prices and reviews on every run | Wholesale-tier, slower refresh | Your own data, not market data | Daily | Quarterly reports |
| Brand monitoring | Yes — MAP, gray market, unauthorized resellers | No (wholesale only) | Your storefronts only | Apps only, no products | Custom engagements |
| JD自营 vs 第三方 flag | Yes | N/A | N/A | N/A | Custom |
| Review counts + ratings | Yes | Limited | N/A | App reviews only | Custom |
| Self-serve API | Apify run = HTTP call | Manual / aggregator APIs | Shopify Admin API | Yes | No |
| Cost | $0.05 / product, pay-per-result | Aggregator $$$, often $5k+/mo | $0 but only your data | $$$$ ($60k+/yr enterprise) | $$$$$ bespoke |
| Cross-border friendliness | Plug-and-play Apify run | Wholesale-oriented | Storefront tool, not intel | Apps not products | Reports, not data |
If you're doing cross-border retail arbitrage, MAP enforcement, or category demand analysis, JD.com Product Scraper gives you live retail-side data that 1688 / Shopify / Sensor Tower / Sinotrust simply don't expose at this price point.
Use cases & playbooks
1. Cross-border arbitrage price floor
Run daily across your top 50 SKUs. Compare current_price_cny × FX × shipping × tariff against your Amazon/TikTok Shop landed cost. Auto-flag SKUs where JD price drops > 10% week-over-week — those are the ones where your retail margins are about to collapse.
2. JD自营 vs 第三方 share-of-shelf
Track the seller_type mix in your category over time. A rising 第三方 share usually signals JD ceding direct distribution to marketplace sellers — which is a sourcing opportunity but also a brand-control risk.
3. Brand-protection / MAP enforcement
For each of your brand's SKUs, monitor seller_name. Flag any non-authorized seller. Combine with current_price_cny to detect MAP violations in real time.
4. Review-velocity demand signal
Track review_count deltas weekly per SKU. SKUs with accelerating review counts are gaining demand momentum — leading indicator for restocks and category trend reports.
5. New-product launch monitoring
Run weekly with sort=default and a tight keyword list. Diff against prior runs to surface newly-listed SKUs. Great for category-launch tracking and competitive intelligence.
Companion actors (NexGenData fleet)
This actor is part of a Chinese-market / Asia-Pacific intelligence stack. Combine it with:
- Weibo Hot Search Tracker — 微博热搜 trending topics, viral signals, and consumer attention as a leading indicator for JD demand.
- Xiaohongshu Trends Tracker — 小红书 / RED note trends — early demand signals for beauty, fashion, lifestyle SKUs before they show up on JD.
- China Trends Tracker — cross-platform Chinese trend aggregator for omni-channel demand mapping.
- Eastmoney China Stock Screener — A-share fundamentals for the listed brands (e.g. JD-listed appliance OEMs) behind the SKUs.
- HKEX Hang Seng Stock Screener — Hong Kong-listed brand parents (JD.com itself, Tencent-backed brands, etc.).
- Finance MCP Server — drop the above into a Claude/GPT agent via MCP for natural-language e-commerce intelligence.
The full stack lets you trace consumer attention (Weibo / Xiaohongshu) → product demand (JD) → brand financials (Eastmoney / HKEX) in a single agent workflow.
Operational notes
- Anti-bot: JD's defenses are real but not draconian. Expect ~85–95% success on normal keywords. Niche or restricted categories (alcohol, pharma) sometimes return shorter result sets.
- Geography: all prices are CNY, China-region pricing. Cross-border ¥-USD conversion is your job downstream.
- Compliance: this actor accesses only publicly-served pages on JD.com. Use responsibly and in accordance with JD's terms and your jurisdiction's data-protection regime.
- Throttling: the actor self-throttles enrichment to 4 concurrent requests. Don't run more than ~5 parallel actor runs against JD or you risk seeing maintenance stubs.
Roadmap
- Multi-category sweep mode (auto-walk a category tree)
- Stock-availability flag (in stock / 预售 / 缺货)
- Historical price tracking via JD's price-history endpoint
- Review-text scraping (premium add-on)
Try it now
Click Run with the default input, get 30 real JD.com products in under 60 seconds, and pipe them into your existing data pipeline. Smoke-tested daily; pricing is transparent and pay-per-result.
Run the JD.com Product Scraper → and start your free trial today. Sign up via the link, run any NexGenData actor, and the free $5 of Apify platform credit covers your first 100 JD products — risk-free. Build your Chinese e-commerce intelligence stack with NexGenData.
Built by NexGenData. Questions or custom data needs? Reach out via the Apify Store contact form.
Why this beats Tmall, 1688, Pinduoduo, and Crunchbase enrichment alone
| Capability | NexGenData JD.com Scraper | 1688 Alibaba Browse | Pinduoduo App | Tmall Open Platform | Crunchbase / PitchBook enrichment |
|---|---|---|---|---|---|
| JD.com SKU-level price + discount | ✅ CNY current + original | ❌ wholesale only | ❌ different catalog | ❌ different storefront | ❌ company-level only |
| JD自营 vs 第三方 seller-type flag | ✅ | ❌ | ❌ | ❌ | ❌ |
JD Logistics flag (is_jd_logistics) | ✅ | ❌ | ❌ | ❌ | ❌ |
| Review count + average rating | ✅ | ❌ public | ⚠️ limited | ⚠️ closed API | ❌ |
| Bulk export (JSON / CSV / Parquet) | ✅ via Apify dataset | ❌ HTML only | ❌ app only | ⚠️ closed merchant API | ✅ but at $1500-3000/seat/mo |
| Cross-border friendly (no Chinese phone / 营业执照) | ✅ public endpoints | ⚠️ partial | ❌ app-only auth | ❌ requires Tmall merchant account | n/a |
| Pricing | $0.05 / SKU, no subscription | Manual browse | n/a | Enterprise contract | $1500-3000/seat/mo flat |
For competitive-intel teams comparing brand presence on China's #2 e-commerce platform, this gives you the same SKU-level depth Bloomberg China Consumer Intel and SimilarWeb Shopper Intelligence charge five-figure annual fees for — without the contract.
About NexGenData
NexGenData publishes 200+ buyer-intent Apify actors covering SEC filings (Form 4 insider buys, Form D, 13F holdings, 8-K material events, Schedule 13D/G activist tracker), YC alumni, Delaware DOC, lead generation, competitive intelligence, stock fundamentals across 30+ exchanges (SGX, ASX, TSX, B3, TWSE, KOSPI, HKEX, BSE Beijing, STAR Market, NSE India, HOSE Vietnam, Saudi Tadawul, JSE), property & macro data (Singapore HDB / URA, US Treasury yields, FX rates, commodity futures), and Chinese-market intelligence (JD, Douyin, Weibo, Xiaohongshu).
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