A nonlinear predictive control algorithm based on wavelet neural network (WNN) integrating optimal experimental design with manifold regularization is presented for the complex processes. Firstly, the wavelet hidden nodes are recursively selected from candidate node set to be added into WNN and the optimal parameters of selected nodes are obtained through extended Kalman filter (EKF). The optimum experimental design and Laplacian regularization are then integrated to select salient WNN hidden nodes, and minimum description length (MDL) is utilized to determine the number of hidden nodes. Initial WNN parameters and associated weight updating scheme are provided via an online Gustafson-kesscl(GK) based fuzzy satisfactory clustering algorithm with intuitive interpretation and physic meaning. Finally, a predictive functional control law is given by linearizing WNN. The simulation of industrial coking equipment shows the efficiency of the proposed algorithm.