Design And Implementation Of Intelligent Customer Service System Based On LSTM

At present,most companies still use manual customer service to deal with customer demands.In the face of a large number of users,the use of manual customer service means a lot of manpower and material resources,especially for companies like China Mobile that have hundreds of millions of users.Using machines instead of labor to complete some routine,repetitive customer service work will greatly reduce the cost of the enterprise.Based on this,this paper designs and implements an intelligent customer service system based on neural network.This paper uses B/S three-tier application architecture to construct the system.The front end uses Vue.js technology,the back end uses Django framework,and the database uses very popular MySQL.Firstly,this paper analyses the background and significance of the development of intelligent customer service system,then makes a detailed demand analysis of the intelligent customer service system,and divides the system into functional modules according to the demand.Then,this paper designs and implements each functional module in detail.Enterprise users can configure business and dialogue processes and assign a small number of manual customer service to business.They can also train semantic matching model independently according to the actual situation.The core work of this paper is the design and training of the neural network semantic matching model.Referring to the current popular neural network model in academic circles,this paper designs the semantic matching model according to the actual business situation,and uses a large number of actual business data training,and achieves very good results.The intelligent customer service system designed and implemented in this paper has the advantages of portability and expansibility.After repeated system testing and feedback from actual use,the intelligent customer service system designed and implemented in this paper runs stably and basically meets the expected design objectives.