Abstract: The quality classification of high-pressure hydrogen energy storage and transportation equipment is very important to ensure the safety of hydrogen energy industry chain. Aiming at the problem that the existing evaluation system is not perfect, this paper constructs a hierarchical early warning model based on anp-topsis and BP neural network. Firstly, according to GBT 33145-2016 standard, 10 key indicators such as tensile strength and number of cycles are selected, and the index weight is determined through ANP; Secondly, the TOPSIS method is used to calculate the equipment proximity and classify the quality level (a/b/c); Finally, BP neural network is trained based on hierarchical data to realize intelligent early warning. The experimental results show that the overall accuracy of the model is 94% in the simulation data experiment, 97% in the measured data comparison experiment, and the recall rate of high-risk level (Level C) equipment is 97% and 96% respectively. The research results provide a theoretical basis and practical tools for the quality control of high-pressure hydrogen storage equipment.