针对传统校园问答系统存在的“信息孤岛”、响应滞后和差异化需求等痛点,提出并实现了一种融合大语言模型与检索增强生成（RAG）的智能问答平台。通过构建分层系统架构,整合知识库构建、混合检索和智能生成三大核心模块,实现知识检索与智能问答的深度融合。数据表明,平台在课程安排、校园政策等高频问题上达到92.2%的意图识别准确率和88.9%的答案准确率,并且支持多角色服务和知识库动态更新,有效满足了师生多样化的信息需求。

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.