Abstract: The performance of underwater target classification and recognition is limited by the selected features,and the application of multi features is usually very helpful for the stability of classification results.To solve this problem,this paper proposes a method of underwater target classification and recognition via joint sparse representation model.Firstly,three kinds of features with information complementarity and correlation are extracted from underwater target echo signals:the central moments feature,the wavelet packet component energy feature and the Mel Frequency Cepstrum Coefficients feature,and then the joint sparse representation model is optimized by using the accelerated proximal gradient method,and the optimal joint sparsity coefficient is obtained,finally the class labels for test samples are determined via the minimum reconstruction error criteria.The simulation experiment is conducted in anechoic tank to classify to identify six kinds of targets.The results show that compared with the traditional algorithm,the proposed algorithm has higher recognition accuracy,and its execution efficiency is greatly improved.