Abstract: The Groin field of spur-dike group is a recirculation region. In the main flow region, the width of cross-section is narrow, the flow velocity increases, and the sediment transport is accelerated.The function indexes of the spur-dike group restricting rivers are: the width of the river and the ratio of width to mean depth in section(\sqrtB / H)at the desiged lowest navigable stage. A prediction model for the function indexes of the spur-dike group was established based on the theory of regression support vector machine. Input factors of the model were the indexes of incoming water and sediment, water surface gradient, riverbed morphology and bed sediment composition, and the output factor was the function indexes of spur-dike group. A trial method was used to determine the insensitivity parameter ε, the penalty constant C and the kernel function parameter σ of this model. The shallow area of Zhangnan waterway was taken as an example and data were collected. The realization of SVM Model training was based on MATLAB programming. The accuracy of the sample display model met the requirements. The verification results show that the relative error of the simulation results is below 10%, which indicates that the SVM prediction model is more effective than the BP artificial neural network model, and the SVM model is practical.