Research On Enterprise Credit Inquiry Model In Grain Spot E-commerce Transaction Based On GA-BP Neural Network

In recent years,the rise and development of food e-commerce has added new content and created new opportunities for bulk food spot trading,but it has also brought new hidden dangers.Due to the transaction mode of spot e-commerce and the particularity of food enterprises,some enterprises have a series of credit risk problems such as filling in false information,false delivery,fund arrears,food quality is not up to standard,malicious breach of contract and so on.All kinds of economic dispute cases of food trading occur frequently,which has aroused the high attention of the society.At present,most of the research on credit risk assessment focuses on the bank credit level,and the identification of enterprise credit under the grain spot e-commerce transaction is in a stage to be explored.In view of the above problems,this paper studies the credit risk of grain enterprises based on the characteristics and development status of grain spot e-commerce transactions.On food spot electricity trading under the comprehensive analysis of the factors influencing enterprise credit risk,from a vast amount of index screening can more accurately reflect the status of food enterprise credit risk quantitative index and qualitative index,respectively from the comprehensive qualities of enterprises,enterprise financial condition,the condition of enterprise management and enterprise performance of 26 indexes are selected from four aspects,Constructing credit risk evaluation index system;BP neural network is applied to the construction of the credit model to deal with the nonlinear relationship among the indicators.Aiming at the problem that BP neural network is easy to fall into the local optimal solution,a genetic algorithm based on global search is introduced to optimize the selection of its initial weight and threshold,and GA-BP neural network model is established.On the basis of the traditional genetic algorithm,some parameter Settings are improved,which is more conducive to finding the optimal individual in the population as the initial weight and threshold value of BP neural network,so as to avoid the BP neural network falling into the local optimal solution more effectively,and improve the accuracy of the model.Finally,the collected data are used to carry out simulation experiments and comparative experiments on the established model,and the experimental results are analyzed to verify the effectiveness and accuracy of the model.In this paper,the research on the enterprise credit investigation model in the food spot e-commerce transaction provides a guarantee for the environmental safety of the food e-commerce transaction,and also provides a reference for the further research on the credit risk evaluation of the food enterprises in the e-commerce transaction.