Research of LSTM-RNN Model and Its Application Evaluation on Agricultural Products Circulation

Abstract

Under the background of big data, the traditional time series model can not meet the needs of people to predict the circulation supply chain of agricultural products. The artificial neural network has been widely used in the field of agricultural products prediction with strong nonlinear mapping ability. In this paper, LSTM-RNN neural network model is used to analyze the performance evaluation system of agricultural products circulation supply chain centered on supermarkets. Considering the financial situation, operation ability, growth ability, and customer satisfaction of agricultural products circulation supply chain, the corresponding evaluation index system is established and compared with the traditional BP neural network. It is proved that the LSTM-RNN neural network evaluation method is completely feasible and accurate for the performance evaluation of the 'agriculture-supermarket docking ' circulation supply chain.