The relationship between different radar targets is often nonlinear due to the complexity of target movement and environments, so the recognition rate will decrease when traditional methods for linear dimensionality reduction are used. For this reason, the nonlinear manifold structure characteristics of ISAR 2D images were analyzed in detail, and then the Spatially Smooth Locality Preserving Projections (SSLPP) algorithm of manifold learning was used for feature extraction and dimensionality reduction. Moreover, three kinds of aircraft targets were classified by k-nearest neighbor classification. Compared with other traditional subspace methods, SSLPP algorithm can earn more local information of the image space by considering the spatial relationship between pixels in ISAR 2D image sufficiently. The simulated experimental results suggest that SSLPP algorithm has better classification performance than PCA、LDA and LPP algorithms in ISAR target recognition.