Construction of medical information knowledge graph question and answer system based on deep learning

In view of the shortcomings of existing medical information intelligent question-and-answer systems in terms of data sources, user needs understanding and answer accuracy, this research aims to build a medical information questionanswering system based on deep learning and knowledge graph. Through the utilization of the BERT-GCN model, as well as knowledge graph, intelligent question-and-answer, and network crawler technologies, we have gathered, extracted, and integrated medical data from the Internet extensively. The system's key features include a knowledge graph, keyword matching, and automatic question-and-answer capabilities, facilitating the retrieval and visualization of medical information knowledge. By efficiently answering questions, users are able to access the necessary medical knowledge and knowledge graph in a user-friendly manner. This system effectively assists patients and healthcare professionals in accessing, comprehending, and utilizing medical information, thereby advancing the progress of the healthcare industry