Intelligent question answering system for medical knowledge

In recent years, intelligent question answering system has been widely used in various interactive services. Intelligent question answering system is to arrange the accumulated disordered corpus information in an orderly and scientific way, and establish a classification model based on knowledge to support the question answering of various forms. In this paper, vertical medical website data and electronic medical record data are fused to solve the mapping problem of colloquial medical vocabulary and medical professional vocabulary. PLSA (probabilistic latent semantic analysis) theme architecture model solves the hidden theme content crawling problem of web page crawler. The application of vector space model and knowledge graph in medical knowledge question answering system solves the problem of low efficiency in traditional interactive question answering system. After testing, it can accurately understand the user’s intention, and the accuracy of question and answer is high.