The helicopter, due to its complex structure and intricate functional mechanisms, often encounters quality issues during the assembly process. These issues include non-conforming holes, substandard riveting, and incorrect positioning. Quality prediction is a key support technology for quality control and continuous improvement in the helicopter structural assembly production line. Influenced by deviations and disturbances in assembly process routes, tooling, and measurements, quality issues such as out-of-tolerance or instability in the helicopter structural assembly process frequently occur. Modeling and solving the problem of quality prediction in the helicopter structural assembly process has become an urgent issue. This paper introduces the use of Bayesian networks to model and predict the quality of the helicopter structural assembly process and verifies its effectiveness through actual assembly cases.