Aiming at the robust control problem of control systems under unknown nonlinear dynamics, time-varying disturbances, input saturation and Gaussian noise, an adaptive sliding mode control method deeply integrating event-triggered mechanism and dynamic coupling strategy was proposed. Firstly, the dynamic surface control (DSC) technique combined with radial basis function neural network (RBFNN) was adopted to approximate the unknown dynamics of the system, and a staged error constraint strategy was designed to achieve dynamic collaborative optimization of convergence speed and steady-state accuracy. Secondly, a time-varying disturbance observer was introduced to estimate disturbances such as wave disturbances online, and an intelligent smoothing processing mechanism driven by Gaussian error function was used to handle the problem of asymmetric input saturation. Furthermore, by combining the event-triggered mechanism, the adaptive dynamic threshold control was used to reduce the control update frequency and decrease the consumption of computing resources. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error can converge to a preset neighborhood. Simulation results show that the proposed method significantly improves the system’s anti-interference ability and control efficiency, providing theoretical support for the application of related control theories in engineering practice.