On the basis of multi-class and nonlinear characteristics of hyperspectral remote sensing image database, this paper assumes that hyperspectral remote sensing database have a bundle manifold structure property and proposes a Semi-supervised Bundle Manifold Learning (SSBML) algorithm to effectively extract the discriminant characteristics of hyperspectral remote sensing image. The algorithm uses labeled samples and unlabeled samples to construct two neighborhood graphs to maintain a “whole” structure (the relationship between the various sub-manifolds) of bundle manifold in the data set and the intrinsic structure characteristics in each sub-manifold. By which, it achieves semi-supervised bundle manifold learning. The experimental results on Kennedy Space Center(KSC) and PaviaU hyperspectral database show that the algorithm efficiently discovers the subtle characteristics of the bundle manifold structure in hyperspectral remote sensing database, and enhances the classification accuracy of hyperspectral remote sensing images. For the overall classification accuracy, this algorithm is improved by 2.9%—15.7% as compared with those of Locality Preserving Projection(LPP) and Neighborhood Preserving Embedding(NPE) algorithm based on single-manifold assumptions, and increased by 2.6%—12.4% as compared with those of the Semi-Supervised Maximum Margin Criterion (SSMMC)and the Semi-Supervised Sub-Manifold Preserving Embedding(SSSMPE ) based on semi-supervised algorithms.