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The unified attitude reference is an important guarantee for the maritime combat platform to achieve accurate de-tection and strike. A prediction model combining state-dependent auto-regressive model with radial basis function neural networks is put forward for the problems that the existence of ship angular flexure makes it difficult to set up the unified attitude references. Unlike the current time series prediction methods, the model uses RBF neural net-works to approximate the parameters of SD-AR model, and the parameters of RBF neural networks are estimated with a structured nonlinear parameter optimization method, providing a basis for angular deformation compensation. According to the RBF-AR model, a design of theoretical algorithm and a mathematical simulation are carried out. The simulation results show that the prediction method is better than common time series prediction methods. The prediction model possesses best potential application in the field of ship angular flexure.