Joint sparse representation (JSR) is a commonly used classifier to recognize different objects with core features extracted from images. However, the generalization ability is weak for traditional linear kernel, and the objects with similar feature values affiliated to different categories are not enough distinguished especially for hyperspectral image (HSI). In the paper, a HSI classification technique based on weight wavelet kernel JSR ensemble (W 2 JSRE) model and β-whale optimization algorithm (WOA) is proposed to conduct pixel-level classification, where wavelet function is acted as the kernel of JSR. Moreover, ensemble learning is used to determine the category label of each sample by comprehensive decision of some sub-classifiers, and β function is utilized to enhance the exploration phase of WOA and obtain the optimal weight of sub-classifiers. Experimental results indicate that the performance of proposed HSI classification method is better than that of other newly proposed and corresponding approaches, the misclassification and classified noise are eliminated to some extent, and the overall classification accuracy reaches 95% for entire HSIs.