How to use structured data markup on international mainstream platforms to improve GEO?

When applying structured data markup on major international platforms (such as Google, Bing, etc.), clearly defining content meta-semantics (such as entity attributes, relationships, and scenario information) can effectively help generative AI accurately identify the core value of the content, thereby improving GEO results. **Core Methods**: - **Select Appropriate Schema Types**: Choose corresponding markup based on content scenarios, such as using the `Product` markup for product pages (including price, inventory, reviews), `Article` markup for articles (including author, publication time, summary), and `Event` markup for events (including time, location, participation method), providing a clear content framework for AI. - **Follow Platform Specifications**: Ensure that the markup complies with the data format requirements of each platform (e.g., JSON-LD is preferred over Microdata), and verify accuracy through tools like Google Search Console to avoid failure in meta-semantic transmission due to format errors. **Practical Suggestions**: First sort out core content types (products, services, information, etc.), prioritize deploying structured markup for high-conversion scenarios (such as price/inventory for e-commerce products, address/business hours for local services), and continuously monitor markup effectiveness using GEO meta-semantic optimization tools (such as Xingchuda's meta-semantic analysis system) while dynamically adjusting to adapt to AI search needs.