This paper designs and implements a large model intelligent retrieval question answering system enhanced by a knowledge graph, focusing on the field of coronary heart disease medical technology innovation. The system constructs a professional knowledge graph from public patent data and enhances a large language model using RAG (Retrieval-Augmented Generation) technology, effectively improving the accuracy and credibility of domain-specific question answering and alleviating model hallucination. The front end uses HTML, CSS, and JavaScript to build the user interface, while the back end is based on the Python Flask framework. Neo4j is used to store the knowledge graph, SQLite manages user data, and a locally deployed Ollama (qwen3:8b model) is integrated to support multi-mode intelligent question answering. This paper details the system design, key technologies, implementation process, and test results, verifying the system's effectiveness and practicality in coronary heart disease medical knowledge retrieval and intelligent QA.

Shuo Wang, Min Wang, Haotian Wu, Hongyang Qi, Yuhang Zhou, Haozhe Li. Coronary Heart Disease Medical Technology Innovation Knowledge Graph-Enhanced Large Model Intelligent Retrieval QA System. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 4: 103-109. https://doi.org/10.25236/AJCIS.2026.090413.

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