<p>Active suspension systems serve as critical components in intelligent vehicle chassis, effectively mitigating the adverse effects of harsh road conditions on vehicle stability and ride comfort. However, the increasing intelligence and integration of chassis systems have led to excessive computational and communication resource consumption, complicating system maintenance and parameter tuning. To address these challenges, this paper investigates a novel data-driven robust control methodology for automotive active suspension systems, explicitly accounting for potential actuator faults and event-triggered mechanisms. First, a dynamic model of a half-car active suspension system with actuator faults is established, transforming the control problem into a two-player zero-sum game. Off-policy reinforcement learning is employed to solve the Nash equilibrium solution, and a robust compensation-based fault-tolerant control law is synthesized to counteract actuator failures. This methodology eliminates reliance on explicit model parameters, optimizing system control parameters via neural networks and adaptive update rates. The closed-loop system stability and Zeno-free behavior are rigorously verified using a Lyapunov-based approach. The simulation results verify the effectiveness of the proposed method. The system satisfies <i>H</i>_∞ performance under both bump and stochastic road excitations, saving over 80% of communication resources, significantly reduces communication resource consumption, and ensures ride comfort and stability even under partial actuator failure conditions.</p>