There are two heterogeneous data types in hyperspectral image (HSI): rich spectral data and spatial information. Recent research has shown that the application of spectral–spatial information significantly improves HSI accuracy; thus, multiple-feature combination-based methods have been favored by researchers in the field of HSI classification. A multiple-feature combination approach, based on the particle swarm optimization (PSO) algorithm, is proposed for improving the accuracy of HSI classification. The proposed method couples a multiple kernel support vector machine (SVM) with a PSO algorithm to assign optimal weights to different kernels. Moreover, it also solves the problem of artificially selecting weights when learning multiple features by implementing adaptive weights on different datasets. In addition, it has fewer parameters and a shorter training time than deep learning methods, thus, the model is smaller and easier to train. The proposed method was tested on four datasets, containing two and three kernels. The experimental results show that our optimized method improves the classification accuracy; additionally, the kappa performance of the classification is also better.