Automated WeChat Article Classification and Summarization Workflow

This n8n workflow automates the process of reading, filtering, classifying, and summarizing WeChat articles by integrating Google Sheets, RSS feeds, OpenAI GPT models, and Notion. The goal is to efficiently identify relevant articles, generate insightful summaries in Chinese, and store the results in Google Sheets and Notion for easy review and management.

The workflow begins with manual trigger input and reads initial article links and RSS feed URLs from Google Sheets. It then deduplicates links to avoid redundant processing. The flow fetches content from each RSS feed, cleans and extracts meaningful text content, and filters articles published within the last 10 days.

Next, the core logic involves classifying articles as relevant or not using a language model-based classifier, followed by deep summarization with GPT-4.1-nano. The summarization adds value by producing comprehensive, Slack-formatted insights with detailed analysis in Chinese, making complex articles easier to understand.

Filtered and processed data is then stored in both Google Sheets and Notion, with metadata such as fetch time and publication date. The workflow is practical for content curation, research, or monitoring trends in WeChat articles, especially for Chinese-speaking audiences interested in AI, personal development, or industry updates.

This comprehensive automation enhances efficiency, improves content insights, and streamlines information management for users needing regular content updates and analysis.