This course centers on Xiaohongshu AI-matrix acquisition, first breaking down the platform's traffic logic to move beyond chasing views toward lead capture. Starting from network setup and AI-tool basics, it builds the three-layer ops foundation; teaches a topic library and uses an AI knowledge base to build a sustainable content system. It covers image-text and short-video notes, closes the full acquisition chain, and builds an AI efficiency workflow for steady matrix-account lead acquisition.

It suits solo founders wanting Xiaohongshu leads, bloggers struggling with slow content who want AI batch creation, and operators planning a multi-account matrix for steady targeted clients.

After it, you'll read Xiaohongshu's traffic logic, build acquisition-first ops thinking, finish basic environment setup, fluently use AI tools in a content workflow, build a topic library and AI knowledge base for sustainable content, master image-text and video notes, and run the full acquisition-conversion chain.

Unlike courses that only teach AI writing or Xiaohongshu growth, this chains environment setup, tool combos, topic systems, knowledge-base accumulation and note-making into one pipeline. You won't just make one note—you'll build a content-production mechanism replicable across accounts, letting matrix accounts steadily output while continuously accumulating targeted leads, reducing reliance on personal inspiration and manual creation.

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• 1.Acquisition Logic: Read Platform Traffic—From Views to Leads
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• 2.Build the Foundation: Three Layers for Smooth Acquisition
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• 3.AI Basics: Network Setup & Core AI-Tool Use
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• 4.Build an Acquisition Topic Library & Sustainable Content System
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• 5.Make Image-Text Notes & Run the Full Acquisition Chain
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• 6.Make Video Notes to Level Up Acquisition
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• 7.AI Basics: Connecting the AI Efficiency Workflow
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• 8.AI Knowledge Base: Driving a Content System with a KB
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• Updated Feishu Doc
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💡 Study tip: This tutorial reflects industry practices at the time of release; please align it with the latest platform rules while learning.