Research On Financial Performance Evaluation Of Food Production Enterprises Based On BP Neural Network

With the development of Internet technology and the improvement of domestic innovation,food production enterprises and Internet technologies are gradually integrated,and diversified business models have emerged.The adjustment of people’s consumption habits and diet structures has put forward higher requirements for food production companies.Therefore,the opportunities and challenges faced by food production enterprises coexist.Shareholders need to assess the financial performance of the company to grasp the real situation of the business,and then make strategic decisions.This essay sorts out the relevant theories related to the financial performance evaluation,combined with the characteristics of food production enterprises,add to EVA related indicators based on the existing financial performance evaluation index system,initially constructing financial performance evaluation indicators including five dimensions.47 listed food production enterprises were selected as sample companies to collect the required data according to the index system.The most appropriate values of the two moderation indicators are determined as the industry average values of food production enterprises when the original data are forward processed,so as to establish a model suitable for food production enterprises.Factor analysis method is used to calculate the financial performance score of sample companies,which is taken as the expected output value of BP neural network.Based on the BP neural network can effectively solve the internal complex nonlinear problems,select the BP neural network to establish a financial performance evaluation model.The data of sample companies in2018-2020 were used for learning,training and testing.The results indicate that the established financial performance evaluation model of food production enterprises is effective.Selecting the Manor Ranch as a case company,using the established BP neural network model for financial performance evaluation,further verification of models.