To address the issues of low power capture efficiency, slow dynamic response, and weak interference resistance faced by direct-drive wave energy conversion devices in complex sea conditions, this paper proposes a control algorithm that combines neural networks with model prediction. By enhancing system robustness through a high-precision wave excitation force prediction model and combining it with a rolling optimization algorithm under multi-objective constraints, the device achieves maximum power generation under irregular wave conditions. First, a three-stage fusion prediction model with spatio-temporal feature decoupling capability is constructed. Compared to traditional models, this model reduces the mean squared error and mean absolute error of irregular wave excitation force prediction by 39.96% and 63.39%, respectively, with a temporal fitting accuracy of 98.9%. The prediction model is then embedded into a rolling optimization framework, where high-precision irregular wave excitation force predictions provide feedforward disturbance compensation for control, aligning motor current with the current at maximum power, thereby achieving the goal of maximizing power generation. Experiments demonstrate that the improved model predictive control achieves significant breakthroughs compared to the traditional autoregressive integral moving average model method under two irregular wave conditions (JS and PM) with wave heights of 0.3~0.6 m and periods of 3~6 s: average power increases by 50%~141%, and cumulative energy increases by 38%~189%, validating the significant advantages of the proposed method in enhancing the comprehensive performance and dynamic robustness of direct-drive wave energy conversion systems.