Xiaohongshu Scraper

Extract Xiaohongshu Post and Review Data Without API Access

DataLens extracts post titles, like counts, comment counts, save counts, author usernames, follower counts, tags, and publish dates from Xiaohongshu feeds and search results directly in your browser — no API, no code, no credentials.

Use this page as a starting point for the full workflow: collect the source, clean the table, analyze the patterns, and keep the exportable file together.

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AI scraping workbench

Collect, clean, and deliver without rebuilding the workflow.

DataLens keeps the messy browser step, the structured table, and the exportable files together so analysts, operators, and growth teams can move from live pages to decisions faster.

Collect

Capture live page evidence

Open the source in Chrome and let DataLens detect repeated rows, links, images, comments, or listings on the page you are already viewing.

Clean

Shape messy fields into a table

Use the AI scraping workbench to normalize labels, preserve source context, and keep raw rows beside the cleaned dataset for review.

Deliver

Export files people can use

Send CSV, Excel, JSON, or a research report to the team without stitching together screenshots, scripts, and disconnected spreadsheets.

Post Engagement Metrics

Extract likes, saves, comments, and publish dates from Xiaohongshu feeds and search results in one run.

Creator and Influencer Data

Capture author usernames and follower counts alongside post metrics for influencer and KOL research.

Brand and Product Monitoring

Search for your brand or product keyword on Xiaohongshu, extract the results, and monitor organic reach and sentiment.

Real-world use cases

A brand growth team monitors organic product mentions by searching their brand name on Xiaohongshu, extracting the top posts weekly with DataLens, and tracking sentiment and engagement trends without requiring platform API access or a third-party social listening subscription.

A KOL agency evaluating Xiaohongshu creators exports post-level engagement data for 200 accounts in their target category, normalizes likes and saves against follower counts, and produces a performance tier ranking that informs which creators to brief for a product launch.

A market researcher studying consumer preferences in the beauty category extracts trending posts from Xiaohongshu hashtag feeds, identifies the recurring product attributes and language patterns in high-save posts, and uses the insights to brief a product development team.

How it works

1
Open a Xiaohongshu feed, search results page, or creator profile in Chrome.
2
Launch DataLens — the AI maps the post card structure and field columns automatically.
3
Scroll to load more posts, then export to CSV, Excel, or JSON.

Frequently asked questions

Use these questions to decide how a live website becomes an AI scraping workflow.

What Xiaohongshu data can I extract?

DataLens extracts post titles, like counts, save counts, comment counts, author usernames, follower counts, hashtag tags, and publish dates from Xiaohongshu feeds and search result pages.

Do I need a Xiaohongshu API key?

No. DataLens reads content rendered in your Chrome browser — no API credentials required.

Can I extract Xiaohongshu product reviews?

Yes. Navigate to a product page or review thread and DataLens will detect the post or card structure and extract the visible content.

Can I export Xiaohongshu data to Excel?

Yes. Export any Xiaohongshu extraction as CSV, Excel (XLSX), or JSON — ready for analysis in any spreadsheet or BI tool.

Related workbench workflows

Compare adjacent collection paths by source, export format, and team job.

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