Abstract: Active sonar target classification has important applications and values in both military and civil fields. By using sparse representation theory and combining K-singular value decomposition algorithm with orthogonal matching pursuit algorithm, a sparse representation classification method based on dictionary learning sparse representation classification (DLSRC) is proposed in this paper. Firstly, K-singular value decomposition algorithm is used to train the echo signals of each category of targets and to obtain the category dictionary with target characteristic information, which has good representation ability to signal and contains target category information. Then, the orthogonal matching pursuit algorithm and each category dictionary are used for decomposing the test signals sparsely to obtain sparse coefficients under each category dictionary and to reconstruct signals. Finally, according to the matching degree of each reconstructed signal and reconstruct signals. Finally, according to the matching degree of each reconstructed signal and test signal, the classification accuracy is determined. The results show that when the SNR of 200 test data is -5, -3, and 6 dB, the classification accuracy of DLSRC method for the 200 test data reaches 87%, 89%, and 95.5% correspondingly, which is higher than that of the existing support vector machine (SVM), K-nearest neighbor (KNN) and SoftMax classification methods.