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With the continuous development of the economy, the trade volume of customs clearance by land transportation between Guangdong and Hong Kong is increasing, but there are many problems in customs clearance by land transportation between Guangdong and Hong Kong. Therefore, it is necessary to start with the logistics mode of land transportation between Guangdong and Hong Kong, reduce the cost of customs clearance by land transportation between Guangdong and Hong Kong, and increase the trade volume by land transportation between the two places. This paper systematically analyzes the present situation of customs clearance efficiency in China and introduces BPNN (BP neural network) algorithm to optimize customs clearance. The detailed idea and process of the algorithm are introduced, and the BPNN trained by the algorithm is applied to function approximation. The simulation results show that this scheme can not only improve the convergence speed of the algorithm in the training process but also the trained BPNN has strong adaptive and self-learning ability. Comparing the customs clearance efficiency before and after the improvement, the results show that the improved customs clearance efficiency is higher and can save a lot of time, thus verifying the effectiveness and applicability of the model-solving algorithm. [ABSTRACT FROM AUTHOR]