Prediction of Agricultural Products Logistics Demand in Five Provinces of North China Based on BP Neural Network

Abstract

Agricultural product logistics is the key to ensuring people's livelihood. The prediction of agricultural products' logistics demand is an important guarantee for the rational planning of agricultural products logistics. However, the demand for agricultural product logistics is affected by many factors, which increases the complexity of its prediction. Therefore, taking the logistics demand of agricultural products as the research object, this paper constructs an index system from five aspects: the level of economic development, the level of industrial structure, the level of logistics development, the supply factors of agricultural products and human factors. Using the nonlinear mapping ability of the BP neural network, this paper constructs a BP neural network model to predict the logistics demand of agricultural products and takes the five provinces in North China as an example to predict the logistics demand of agricultural products. The results show that the established model has a strong ability to describe the nonlinear relationship between agricultural products logistics demand and its influencing factors, and can provide a basis for rational planning and policy-making of agricultural products logistics.