Abstract
From raw material storage through final product distribution, a cold supply chain is a technique in which all activities are managed by temperature. The expansion in the number of imported meat and other comparable commodities, as well as exported seafood has boosted the performance of cold chain logistics service providers. On the basis of the standard basic pursuit (BP) neural network, a rough BP particle swarm optimization (PSO) neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers. To reduce duplicate information in the original data and make the input index more compact, the model employs rough set. Instead of using gradient descent to train the weights of the neural network, particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network's generalization capacity is improved. Finally, an example is presented to demonstrate the model's validity and viability. The findings reveal that the model's prediction error is 40.94 percent lower than the BP neural network model, and the prediction result is more accurate and dependable, providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.