Research on Food Security Risk Assessment and Early Warning in China Based on BP Neural Network Model

Abstract

China has always attached great importance to food security issues; especially in today’s changeable world, it is particularly important to build a feasible and accurate food security early warning system. According to the influencing factors in food security, this paper uses the PCA method and the AHP method to construct a food security early warning index system that includes 4 secondary indicators and 13 tertiary indicators of total security, trade security, ecological security, and food security. There are four security levels of no warning, light warning, moderate warning, and heavy warning, and finally the comprehensive evaluation of food security from 2000 to 2019 and the specific early warning levels of various indicators are obtained. This paper constructs a food security evaluation system from the perspective of data, breaks through the limitations of existing research, and improves the completeness of food security early warning indicators. Because the BP neural network is a multilayer feedforward neural network with strong adaptability, it is one of the most widely used and successful neural network models at present. Finally, BP neural network is used to simulate China’s food security early warning system and design standardized risk prevention and control processes and classified response strategies—routine monitoring, risk control, and emergency response—to provide signal guidance and reference for China’s food security to respond to risks early.