| At this stage,the number of cardiovascular disease patients in my country is showing a continuous upward trend,and the prevention and treatment of cardiovascular disease are urgent.Combining the fast-developing natural language processing and artificial intelligence technology,relying on the massive data and information related to cardiovascular diseases on the Internet,the intelligent question answering system has become an efficient and accurate information retrieval model,providing the general public and patients with access to cardiovascular disease related knowledge.A faster and more convenient channel.Based on the Internet cardiovascular question and answer data,this article deeply researches the question analysis technology in the intelligent question answering system,aiming to design and develop an intelligent question answering system for cardiovascular diseases.The main research contents of this paper are as follows:1.Requirement analysis and architecture design of question answering system.This paper first analyzes the functional requirements and performance requirements of the question-answering system.The main function of the question-answering system is to quickly provide answers to questions related to the cardiovascular field raised by users,so it has a certain degree of stability,real-time,security,and other performance of the system.Claim.Then based on-demand analysis,the system is designed with architecture and functional modules,and the business process of the entire system is designed to provide the basis for the implementation of the subsequent question and answer system.2.Disease text classification based on dual-channel neural network.This paper proposes a question processing flow of question classification first and then question similarity calculation,which can greatly improve the efficiency of question similarity matching.At the question classification level,a question classification model based on a dual-channel neural network is proposed.The local features of the questions extracted by CNN and the sequence features of the questions extracted by Bi LSTM combined with the attention mechanism jointly participate in the question classification process.After the classification is completed,proceed to the next question similarity calculation and matching.3.Text similarity calculation based on multiple related information interactions.This paper proposes an improved method for calculating the similarity of question sentences.The similarity matching is carried out from three granularities of text word vector expression,text characteristics,and interactive features between texts,which can obtain rich deep hidden information.In addition,the above-mentioned methods have been tested on multiple data sets,and the results show that each method has different improvements compared to the benchmark method.4.Design and implementation of an intelligent Q?A system for cardiovascular diseases.Based on the above research,we designed and implemented a Q?A system applet to realize the Q?A interaction on mobile.Firstly,a Q?A database in the field of cardiovascular diseases was constructed,and the Q?A data was collected from several Internet consultation platforms through a web crawler,and the crawled content was cleaned and then stored in the database as the data source of the Q?A system.The system adopts a front-end and back-end development model.The back-end of the applet uses Flask,a Python-based web framework,to complete the pre-processing and parsing of questions and answers,storage of Q?A data,and other business functions;the front-end of the applet uses Vue.js-based uni-app framework to build a cross-platform application to realize the interaction interface between users and the system.In summary,the research work in this article has certain practical significance and application value for the answer and popularization of knowledge in the field of cardiovascular diseases and provides a feasible technical solution for intelligent questions and answer in the field of biomedicine. |
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