Abstract: In order to reduce the dimension of feature effectively on the basis of ensuring the accuracy of target recognition, a feature selection method based on combining support vector machine recursive feature elimination (SVM-RFE) algorithm and cat swarm optimization (CSO) algorithm is proposed in this paper. The method is applied to feature selection of underwater acoustic target recognition. Experimental data processing results show that:compared with SVM-RFE and CSO feature selection algorithms, the average feature dimension of the proposed method is reduced by 8%, and the average target recognition rate is improved by 1.88%. This method also has a certain application value in judging whether the feature is suitable for specific target recognition or not.