In order to solve the problem of high computational cost of model predictive control when solving nonlinear optimization problems in large nonlinear systems such as wastewater treatment, this paper proposes a reduced-order neural network model predictive control algorithm applied to wastewater treatment benchmark. First, for large-scale nonlinear and strongly coupled systems in wastewater treatment, the intrinsic orthogonal decomposition method is used to construct a reduced-order process model to reduce the complexity of the nonlinear system. Then, the long short-term memory network is used to approximate the reduced-order system, thereby solving the problem that the reduced-order system is difficult to express explicitly. Finally, a model predictive controller is designed based on this reduced-order system to achieve efficient control of wastewater treatment. Experimental results show that while ensuring good control effect, the proposed reduced-order neural network model predictive control strategy significantly reduces the computational time compared with the model predictive control strategy of the first principle model of wastewater treatment.