In this chapter, an RNN-based spatiotemporal approach is developed to realize the robust modeling of nonlinear DPSs. Generally, the space neighboring points in a DPS interact each other by means of energy transfer, also named “spatial dynamics”. In this model, its hidden layer at each time is designed to represent the spatial dynamics using a bidirectional RNN (BRNN). The BRNN can represent these adjacent spatial points using a forward step and a backward step represents the interaction between neighboring hidden layers. Then, in combination with all hidden layers of the SRNN at the whole time, the temporal dynamics of the snapshots is exhibited and represented. In this way, this SRNN integrates the spatial/temporal dynamics together and is without requirement of model reduction. Furthermore, a solving approach is proposed to obtain the optimal value of the model coefficients. Convergence analysis and case study prove that the proposed method can effectively reconstruct the nonlinear spatiotemporal dynamics of the nonlinear DPS.