In order to find the complex rules in the data, avoid the blindness of kernel function selection and local optimal nonlinear optimization problems, this study proposes an improved gray wolf algorithm to optimize the multi-core support vector regression machine algorithm. Firstly, a multi-core SVM oil production speed prediction model is constructed based on the global kernel function and the local kernel function. Secondly, the gray wolf optimization algorithm is improved based on the cloud model and the quadratic interpolation algorithm to optimize the selection of the weights and parameters of the kernel function. Finally, the influencing factors set of oil production speed is determined by the grey correlation analysis theory and used as the multi-core SVM prediction model. Compared with 6 kinds of prediction methods of oil production rate, the proposed method has the advantages of better global optimization ability and higher prediction rate.