摘要
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.
作者
范贵进
孙颖颖
FAN Gui-jin;SUN Ying-ying(Fujian College of Water Conservancy and Electric Power,Sanming 366300,China)
出处
《电脑与电信》
2025年第4期65-68,80,共5页
Computer & Telecommunication
基金
福建水利电力职业技术学院2024年度校级科研项目,项目编号:YJKJ2401A。
