- Xiaohongshu (小红书 — known globally as RedNote) has become the single most influential product discovery, beauty, luxury, and lifestyle search engine for Gen-Z and Millennial consumers across Asia and the global diaspora. Over 300 million monthly active users treat RedNote not just as a social feed, but as their primary product review and shopping search engine before buying cosmetics, apparel, electronics, travel, or wellness products.
For global consumer brands, hedge funds, market research teams, and KOL marketing agencies, Xiaohongshu data is pure gold:
- Early Trend Detection: Viral skincare ingredients, fashion silhouettes, and beverage trends break on Xiaohongshu 3 to 6 months before they hit TikTok or Amazon.
- KOL & Creator Vetting: Identifying high-conversion micro-influencers by analyzing real engagement-to-follower ratios (
likes,collects,comments,shares) rather than vanity follower counts. - Unfiltered Consumer Sentiment: Reading nested comment threads to see why buyers love—or return—a competitor's product.
The obstacle? Xiaohongshu does not offer a public data API, and its web and mobile interfaces enforce strict SMS login walls and aggressive rate limits after just a few clicks.
In this guide, we break down how teams extract clean, structured Xiaohongshu data at scale in 2026—whether you want a Done-For-You Excel/CSV dataset delivered to your inbox or a self-serve cloud API for your data pipeline.
What Data Can You Extract from Xiaohongshu (RedNote)?#
Depending on your business use case, Xiaohongshu extraction falls into four core data feeds:
| Data Feed | Key Extracted Fields | Primary Business Use Case |
|---|---|---|
| 1. Keyword Search & Viral Notes | note_id, title, desc, type (video/image), liked_count, collected_count, comment_count, share_count, image_urls, video_url, tags | Trend discovery, share-of-voice tracking, competitor brand monitoring |
| 2. Creator & Influencer Profiles (KOLs) | user_id, nickname, red_id, fans_count, follows_count, liked_and_collected_count, bio, ip_location, verified_badge, recent portfolio notes | Influencer discovery, KOL vetting, agency campaign benchmarking |
| 3. Deep Comment & Reply Trees | comment_id, content, like_count, sub_comment_count, user_info, ip_location, created_time, nested replies | Customer sentiment analysis, product defect discovery, AI/LLM training |
| 4. All-in-One Multi-Surface Extraction | Unified search + profile + post detail + media URLs in a single run | End-to-end brand intelligence & automated data warehouse ingestion |
Why Traditional Scraping Fails on Xiaohongshu#
If your engineering team has tried to scrape xiaohongshu.com using standard headless browsers or Python scripts, you have likely encountered three immediate roadblocks:
1. Mandatory +86 SMS Login & QR Code Walls
Unauthenticated visitors can view at most one or two landing pages before Xiaohongshu blocks search results and comment drawers behind a QR/SMS login modal. Even if you register an account with a phone number, automated browsing triggers immediate account restrictions or SMS re-verification prompts within a few dozen requests.
2. Strict Anti-Bot & Rate-Limit Controls
Xiaohongshu's web and mobile endpoints actively block basic automated scripts and unverified cloud requests within minutes, making DIY scrapers brittle and expensive to maintain.
3. Ephemeral Media & CDN Links
High-resolution carousel images and watermark-free video streams use expiring links if not properly normalized. Extracting broken or low-resolution thumbnail links ruins downstream computer vision and creative analysis pipelines.
Two Ways to Get Xiaohongshu Data Today#
At UnitBytes, we support both non-technical business teams (who just want clean spreadsheets) and engineering teams (who want automated API pipelines):
Option B: Self-Serve Cloud Extraction via Python & Apify#
If you want to run extractions on-demand or schedule daily feeds into Snowflake, BigQuery, or Amazon S3, UnitBytes maintains 4 specialized Xiaohongshu cloud engines on Apify with zero account or proxy setup required:
- RedNote (Xiaohongshu) All-in-One Scraper: Search keywords, extract full post details, media URLs, and author metadata in one unified engine.
- Xiaohongshu Trend & Fast Search Scraper: High-speed keyword and category trend discovery starting at $0.001 / post.
- Xiaohongshu Creator & Influencer Profile Scraper: Extract KOL follower counts, total likes/collects, bios, and creator note portfolios.
- Xiaohongshu Comments & Sentiment Scraper: Extract deep top-level comments and nested replies for consumer sentiment analysis.
Quickstart: Extracting Viral Xiaohongshu Notes in Python
Install the official Apify Python client:
pip install apify-client pandas
Run a keyword search for trending skincare or fashion posts and export directly to a CSV spreadsheet:
import pandas as pd
from apify_client import ApifyClient
# Initialize the ApifyClient with your API token
client = ApifyClient("YOUR_APIFY_TOKEN")
# Configure search keywords and volume
run_input = {
"keywords": ["极简护肤", "秋季穿搭", "防晒测评"],
"maxItems": 200,
"sort": "popularity_descending"
}
# Trigger the UnitBytes Xiaohongshu All-in-One Engine
run = client.actor("unitbytes/xiaohongshu-scraper").call(run_input=run_input)
# Load results into a Pandas DataFrame and save to Excel / CSV
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
df = pd.DataFrame(items)
df.to_csv("xiaohongshu_brand_trends.csv", index=False, encoding="utf-8-sig")
print(f"Exported {len(df)} Xiaohongshu notes to xiaohongshu_brand_trends.csv")
utf-8-sig)
Always save Chinese/APAC CSV files using encoding="utf-8-sig" (UTF-8 with BOM) so Microsoft Excel opens Chinese characters, emojis, and pricing symbols cleanly without garbled text.Sample Output Schema#
Every extracted Xiaohongshu record is normalized into a clean, flat-friendly JSON structure ready for BI dashboards or LLM pipelines:
{
"note_id": "66f19a8c000000001e038d21",
"title": "2026早C晚A护肤公式｜敏感肌实测28天对比",
"type": "normal",
"liked_count": 14820,
"collected_count": 9430,
"comment_count": 612,
"share_count": 1205,
"author": {
"user_id": "5c9e1120000000001201a9f4",
"nickname": "成分党林博士",
"ip_location": "上海",
"fans_count": 184500
},
"tags": ["早C晚A", "敏感肌护肤", "精华测评"],
"note_url": "https://www.xiaohongshu.com/explore/66f19a8c000000001e038d21",
"scraped_at": "2026-10-15T09:30:00Z"
}
How Brands Calculate Engagement & Viral Velocity on RedNote#
Unlike Western platforms where Likes dominate, Xiaohongshu's recommendation algorithm weights Saves / Collects (collected_count) and Comments (comment_count) heavily as purchase-intent signals:
- Save-to-Like Ratio (
collected_count / liked_count): A ratio above 0.45 indicates high "reference value"—users are bookmarking the post as a shopping list or tutorial to buy later. - Creator Efficiency Score: Comparing a note's total interactions (
likes + collects + comments) against the creator'sfans_countreveals whether a post achieved organic algorithmic breakout beyond the influencer's existing follower base.
Need a Custom Xiaohongshu Dataset or Automated Brand Monitor?#
Whether you need a one-time CSV export of 10,000 viral notes for a market research report, a monthly KOL leaderboard, or a daily automated feed delivered to your team's Google Sheets or Snowflake warehouse, you can request a custom dataset directly below.
Extract Xiaohongshu Viral Notes, KOLs & Comments — Zero Accounts
Extract viral posts, creator portfolios, engagement ratios, and nested comment sentiment without +86 phone numbers or app logins. Run self-serve on Apify or request a Done-For-You Excel/CSV export below.