Application Research Of Teaching And Learning Enhanced Whale Optimization Algorithm

With the rapid development of network and artificial intelligence technology,automatic question answering(QA)system has become the current trend of humancomputer interaction.Health and medical issues have always been the most concern for people's lives,and they can easily share and access a large amount of medical information on the Internet.As a key technology of knowledge-driven artificial intelligence,knowledge graph can not only organize and manage massive Internet information,but also understand information in the way of associative thinking.This paper designed and developed an auxiliary diagnosis system based on medical knowledge graph,to help users choose the accurate answer from massive medical data.Auxiliary diagnosis system in this paper the research content mainly includes the following several aspects:(1)How to build a high-quality medical knowledge graph based on graph database,which is mainly divided into three aspects:knowledge collection,data cleaning and knowledge storage.(2)Research how to implement the question and answer task of the auxiliary diagnosis system.First,aiming at the lack of QA training corpus and QA corpus tagging data,this paper constructs the original training corpus and tagging data through language template.On the basis of the data,use the Bi LSTM-CRF model to realize precise medical entity recognition,and then through the LSTM + CNN combination model deal with the issue of user intention question analysis,realize user questions semantic parsing,To establish the mapping relationship of knowledge graph search,in the knowledge graph of the answer in the retrieval.For the records that do not exist in the knowledge map,the improved knowledge completion algorithm Cp Trans E is used to predict;(3)Establish a self-service medical consultation service platform.The web application framework Django is used to package the auxiliary diagnosis and treatment system,and the service is opened by browser to realize human-computer interaction.Finally,the platform can realize the functions of auxiliary query,medical professional knowledge retrieval and visualization.Through the above work,this paper establishes a Chinese medical knowledge graph with high quality and effectiveness.Uses deep learning method to improve the understanding ability of the Intelligent QA System Based on the medical knowledge graph to the natural language questions input by users.Finally realizes a self-access diagnosis and treatment platform to meet the needs of users.