There are many redundant, irrelevant and noise features in underwater acoustic data feature, which reduce the recognition accuracy of underwater acoustic target. To solve this problem, this paper proposes a new feature selection algorithm for underwater target recognition—unsupervised feature selection algorithm based on elastic-net regression (UFSER). The proposed al? gorithm uses spectral regression to calculate a regression coefficient matrix between high-dimensional original underwater acoustic data and its low-dimensional representation. In the regression framework, an elastic-net penalty term is applied to optimize the re? gression coefficient matrix; finally, the classification performance of features is evaluated with the regression coefficient matrix spar? sification. In this paper, feature selection and classification experiments are performed by using the measured underwater acoustic dataset and the UCI sonar dataset. The experimental results show that the number of features is reduced by 60.6% and 60%, while the classification identification accuracy is improved by 1.05% and 6.6% compared with the accuracy before feature selection.