Traditional Online Public Access Catalogues (OPACs) are becoming less effective due to the rapid growth of scholarly literature. Conventional search methods, such as keyword indexing and Boolean queries, often fail to support efficient knowledge discovery. This paper proposes a Smart OPAC framework that transforms traditional OPACs into intelligent discovery systems using artificial intelligence and knowledge graph techniques. The framework enables semantic search, thematic filtering, and knowledge graph-based visualization to enhance user interaction and exploration. It integrates multiple open scholarly data sources and applies semantic embeddings to improve relevance and contextual understanding. The system supports exploratory search, semantic navigation, and refined result filtering based on user-defined themes. Quantitative evaluation demonstrates improvements in retrieval efficiency, relevance, and reduction of information overload. The proposed approach offers practical implications for modernizing digital library services and supports next-generation research workflows. Future work includes user-centric evaluation, personalization, and dynamic knowledge graph updates.

翻译：传统联机公共检索目录（OPAC）因学术文献的快速增长而效能下降。基于关键词索引和布尔查询的传统检索方法难以支持高效的知识发现。本文提出智慧OPAC框架，利用人工智能与知识图谱技术将传统OPAC转变为智能发现系统。该框架通过语义检索、主题过滤和知识图谱可视化增强用户交互与探索能力。系统整合多个开放学术数据源，并应用语义嵌入技术提升相关性和语境理解能力。系统支持探索性检索、语义导航及基于用户定义主题的精细化结果筛选。定量评估表明，该方法在检索效率、相关性及减少信息过载方面均有提升。该方案对数字图书馆服务现代化具有实践价值，并支持下一代研究工作流程。未来工作包括用户中心评估、个性化推荐及动态知识图谱更新。