Optimisation study of agricultural product circulation standard system in big data environment based on DNN model

This paper combines deep learning and neural networks to construct a prediction model of deep neural networks. Taking the bulk agricultural products in X province as an example, it elaborates on the current situation of agricultural product circulation in X province and analyzes the problems in the development process. Using the DNN prediction model for the demand of the agricultural product circulation system, the demand of the agricultural product circulation system is modelled and simulated. After analysis, it can be seen that 588 national industry standards in the area of commerce circulation and domestic trade have been formulated at present. With the increasing production of bulk agricultural products as well as the total food production in X province, the development of agricultural product circulation main body also grows to 3,033 in 2023. However, the time currently used for the distribution chain still accounts for 95% of the total system. Therefore, the layout design scheme derived from the predicted values of the DNN model makes the agricultural product logistics network more reasonable.