Design and Implementation of a Regional Culinary Community Platform with AI-Assisted Question Answering: A Case Study of Xunwei Zhanjiang

Digital platforms for regional culinary culture need to connect information about local dishes and places with community participation and accessible exploration services. This study presents the design and implementation of Xunwei Zhanjiang, a community-oriented web platform that integrates regional food discovery, user interaction, and AI-assisted culinary enquiry. Built with Next.js, React, Drizzle ORM, and MySQL, the platform organizes culinary information through district-based displays and structured food cards, alongside community features for sharing posts, commenting, bookmarking, and following users. A server-side integration with Dify enables natural-language enquiries about local cuisine and suggested food itineraries. The system incorporates standardized API responses, input validation, cookie-based authentication, and database uniqueness constraints to support consistent interactions and prevent duplicate social relationships. Preliminary functional checks confirmed successful operation of selected content retrieval endpoints, user login and profile retrieval, access restrictions, and the integrated AI enquiry interface. These observations support the feasibility of bringing structured culinary content, community interaction, and conversational assistance together within a regional web application. However, they do not establish improvements in recommendation quality, cultural engagement, or performance at scale. Further work will address implementation gaps and evaluate response quality, system performance, and usability through systematic testing and user studies.