In support vector machine (SVM) hyperspectral image classification,monocyte function has its limitation. In order to improve the classifier accuracy and generalization ability of SVM model,a complex kernel function SVM using the convex combination of radial basis function kernel and sigmoid kernel was constructed,and it proves that the function satisfies a judgment called Mercer condition as a kernel function. Then,the convex combination of kernels SVM was applied to hyperspectral image classification,and the modeling and experimental validation were completed. The experimental results show that the convex combination kernel has better robustness. As the classification accuracy and KAPPA coefficient have been effectively improved compared to that of the single-core SVM,the new SVM is an effective solution to the problem of multi-classification.