The improved BP neural network supply chain performance evaluation model under the cross-border e-commerce marketing chain

Abstract

With the rapid development of Internet technology, foreign trade has been integrated with it, resulting in the rapid development of cross-border e-commerce, and for all kinds of enterprises to bring rich profits. However, in the fierce market competition, many enterprises ignore the importance of supply chain in the process of operation, which leads to the frequent bankruptcy of enterprises. To solve this problem, the research focuses on the supply chain performance evaluation of cross-border e-commerce enterprises, and proposes an improved error inverse propagation algorithm supply chain performance evaluation model. The results show that the model has improved the service capability of cross-border e-commerce, the performance of suppliers and the supply chain. The average relative error of the artificial neural network algorithm and the error reverse propagation algorithm is 3.26% and 10.23% respectively, while the average relative error of the expected output and actual output of the artificial neural network algorithm is 2.11%, and the average relative error of the expected output value and actual output of the error reverse propagation algorithm is 6.78%. It can be seen that the artificial neural network algorithm can effectively improve the performance level of the supply chain, and under this algorithm, the objectivity of the weights and the accuracy and efficiency of the prediction results are guaranteed. Therefore, this study has important scientific value and practical significance for understanding and improving the supply chain management of cross-border e-commerce enterprises.