To address the identification and prioritization of Safety of the Intended Functionality (SOTIF)-related risks for Automatic Emergency Braking (AEB) systems in complex road environments, a triggering-condition superposition assessment method integrating Bayesian networks and multi-index scoring is proposed. 4 triggering conditions (weather condition, road surface condition, traffic interaction behavior and camera frame rate) are selected. A probabilistic propagation model is constructed from triggering conditions to perception degradation, braking-margin reduction and hazardous events. A scenario-importance scoring system is established by incorporating severity, exposure, controllability, perception degradation and coupling complexity. 3 types of straight road environments are considered, including clear–dry, moderate rain–wet/slippery as well as heavy rain with ponding and low adhesion coefficient. Under each road surface category, 3 types of interaction behaviors(pedestrian crossing, lead vehicle emergency braking and vehicle cut-in) are designed. 3 camera frame rates (30 fps, 15 fps, and 10 fps) are set, forming 27 combined simulation scenarios. The simulation results show that deteriorating weather and reduced road adhesion significantly weaken the braking capability of the AEB system, while a lower camera frame rate increases target-confirmation delay and further compresses the system safety margin. Among the scenarios, heavy rain with ponding and low adhesion coefficient road – pedestrian crossing has the highest risk level, and the lead vehicle emergency braking and pedestrian crossing scenarios under moderate rain – wet/slippery road conditions also exhibit borderline risk. The results demonstrate that the proposed method can effectively identify high-risk superposed scenarios of AEB from the SOTIF perspective and can provide a reference for scenario-library construction, virtual-test prioritization, and safety argumentation.