The invention discloses a one-dimensional range profile recognition method based on self-adaptive locality sparsity preserving projection. According to the method, actually measured one-dimensional range profile signal samples are preprocessed; a sparsity coefficient matrix is obtained through sparsity preserving projection (SPP), and a locality similarity matrix is obtained by locality preserving projection (LPP); sparsity preserving projection equations, locality preserving projection equations and a self-adaptive maximum margin criterion are fused, a joint constraint equation set is established, and a self-adaptive locality sparsity preserving projection matrix is obtained; and training samples and test samples are projected into lower-dimensional space through the projection matrix, and a support vector machine is used to carry out training and classification thereof. Based on the sparsity preserving projection, the locality preserving projection and the self-adaptive maximum margin criterion, the method makes full use of sparse reconstruction of the samples and recognition information contained in neighbor relations and combines with the self-adaptive maximum margin criterion to extract lower-dimensional features of the samples, the recognition accuracy of one-dimensional range profile signals is improved, the feature dimensionality is reduced, and the noise immunity is enhanced.