✨ Description|描述#
These APIs provide access to content, creator, and engagement data from Xiaohongshu (RedNote).
They are designed for retrieving notes, users, search results, feeds, and discussions (comments and replies).这些接口用于获取小红书(RedNote)的内容、用户与互动数据。
主要覆盖笔记、用户、搜索、推荐流,以及评论与评论回复等讨论数据。
🧩 Main Endpoints|主要接口#
🧑 User Profile API
Retrieves user profile information and public counters such as follower count and like count (when available).
获取用户基础资料信息,并可返回粉丝数、获赞数等公开指标(如可用)。
🗂️ User Notes List API
Retrieves a paginated list of notes published by a specific user.
分页获取指定用户发布的笔记列表,可一页一页拉取该用户的全部笔记。
📝 Note Detail API
Retrieves full note description, engagement metrics (likes/comments/collects/shares), and media download URLs for images/videos.
获取完整笔记内容、互动数(赞/评/收藏/分享等),并返回图片/视频下载链接。
💬 Note Comments API
Retrieves note comments with pagination (version behavior may vary by endpoint version).
分页获取笔记评论数据(不同版本的分页能力可能不同)。
🧵 Comment Replies API (Second-level Comments)
Retrieves replies under a specific comment (comment thread / second-level comments).
获取某条评论下的回复(二级评论/评论楼中楼)。
🔎 Note Search API
Searches notes by keyword and optional filters, returning paginated results.
根据关键词及可选条件搜索笔记,返回分页搜索结果。
👤 User Search API
Searches users/creators by keyword, returning paginated results with basic profile signals.
根据关键词搜索用户/博主,返回分页结果与基础资料信息。
🏠 Home Feed API
Retrieves the home feed stream (recommended notes) with pagination.
获取首页推荐流数据(推荐笔记),支持分页获取。
🔗 Share Link Convert API
Converts a shared short link into a normal note URL, typically used to extract the note ID.
将分享的短链接转换为正常笔记链接,通常用于从分享链接中提取笔记ID。
💡 Search Keyword Recommendations API
Returns recommended/related keywords for a given input keyword prefix (search autocomplete).
搜索关键词推荐接口:输入关键词前缀,返回联想/推荐关键词(搜索框提示)。
🎯 Key Use Cases|主要应用场景#
📈 Trend & content research: keyword/topic exploration, discovering what’s popular
内容研究与趋势分析:关键词/话题研究,发现热门内容与趋势
🧑💼 Creator / KOL monitoring: tracking creator profiles, notes, and engagement changes
博主/KOL监控:监控账号资料、笔记发布与互动指标变化
🧠 Brand reputation & sentiment: analyzing notes, comments, and reply threads
品牌舆情与情感分析:基于笔记、评论与二级评论做口碑与情绪分析
🧱 Dataset building & automation: building structured datasets for analytics/reporting pipelines
数据集与自动化采集:构建结构化数据,用于报表、看板与数据流水线
🎞️ Media collection: obtaining image/video download URLs for downstream processing or archiving
素材采集:获取图片/视频下载链接,用于处理、归档或内容分析
Modified at 2026-04-19 16:04:43