In recent years the cold chain logistics of fresh agricultural products has gradually become a research hotspot, and there are many problems in cold chain logistics, such as high cost, backward cold chain logistics information statistics, and many factors affecting demand forecasting. In this paper, the output of cold chain agricultural products is used as the predictor index to establish an index system of influencing factors of cold chain logistics demand for agricultural products, and the cold chain logistics demand of agricultural products in China from 2024 to 2026 is predicted by using the combination of grey correlation analysis method and BP neural network model. The prediction results show that the prediction accuracy of the proposed model is high and the fitting is good, and the predicted data can accurately reflect the growth trend of China’s agricultural product cold chain logistics demand in the next three years, which provides a theoretical basis for logistics planning of government departments and related logistics enterprises.