A Cloud Computing-Based Intelligent Forecasting Method for Cross-Border E-Commerce Logistics Costs

Abstract

Aiming at the problems of poor forecasting effect and low accuracy and low efficiency in current cross-border e-commerce logistics cost prediction methods, a cloud computing-based intelligent method for cross-border e-commerce logistics cost prediction is proposed. Analyze cloud computing concepts, characteristics, and service models, study cloud computing-related technologies, and train BP neural network algorithms based on BP neural network principles. The BP neural network structure is obtained by determining the number of neurons in the input layer, the number of neurons in the hidden layer, the number of neurons in the output layer, and the activation function of the neural network. Normalize the input data samples of the input layer, and select the initial weight, threshold, and learning rate parameters of the BP neural network to determine the momentum coefficient. This paper uses neural network model combined with Spark cloud computing platform to realize the intelligent prediction of cross-border e-commerce logistics cost. This method has good predictive ability. After a large amount of data input and output relationship training, it has obtained the most suitable model for prediction. The experimental results show that the cross-border e-commerce logistics cost prediction effect of the proposed method is good, and it can effectively improve the accuracy and efficiency of cross-border e-commerce logistics cost prediction.