Aiming at the problem of low recognition rate of single-kernel Support Vector Machine (SVM) for ship target classification in Synthetic Aperture Radar (SAR) image, a method for ship target recognition based on multi-feature extraction and Multi-Kernel Learning (MKL) SVM is proposed, which improves the accuracy of target recognition from two aspects of feature extraction and classifier training.First, the public data set is selected to extract the multiple types of features of the ship target, and then weighted fusion is made to the multi-kernel functions to construct a multi-core SVM model.Finally, multi-feature data are used to train and recognize the ship target.In view of the information redundancy problem in multiple sets of target features, the correlation coefficient is used to remove some highly redundant features and reduce the feature dimensions.The Particle Swarm Optimization (PSO) algorithm is used to solve the problem of kernel parameter selecting of the SVM kernel function.The experimental results show that the proposed method effectively improves performance of ship target recognition, and the comprehensive recognition rate is increased from 87.18% of the traditional SVM to 92.31%.