This paper addresses the pain points of traditional campus Q&A systems, such as "information silos", response delays, and differentiated needs, by proposing and implementing an intelligent Q&A platform that integrates Large Language Model with Retrieval-Augmented Generation (RAG). Through constructing a hierarchical system architecture, the platform combines three core modules - knowledge base construction, hybrid retrieval, and intelligent generation - to achieve deep integration of knowledge retrieval and intelligent Q&A. Data demonstrates that the platform achieves 92.2% intent recognition accuracy and 89.9% answer accuracy for high-frequency queries like course schedules and campus policies, while supporting multi-role services and dynamic knowledge base updates.