Abstract In the context of the deep integration of artificial intelligence and industrial fields, precise calculation of multi-step process capability has become a current research hotspot. In most industrial fields, the estimation of multi-step process capability is mostly in the rough estimation stage, and there are relatively few precise quantitative calculation methods. This paper focuses on multi-step processes in the industrial field, using deep learning models to learn the features of each step step step by step, and then comprehensively estimating the weights between each step, ultimately achieving accurate prediction of multi-step process capabilities. This paper conducts in-depth analysis of the performance and efficiency of different models on such problems by designing a large number of validation experiments, and also provides ideas and suggestions for subsequent research in this field.