Convolutional　neural　networks　(CNN)　have　achieved　excellent　performance　for　the　hyperspectral　image　(HSI)　classification　problem　due　to　better　extracting　spectral　and　spatial　information.　However,　CNN　can　only　perform　convolution　calculations　on　Euclidean　datasets.　To　solve　this　problem,　recently,　the　graph　convolutional　neural　network　(GCN)　is　proposed,　which　can　be　applied　to　the　semisupervised　HSI　classification　problem.　However,　the　GCN　is　a　direct　push　learning　method,　which　requires　all　nodes　to　participate　in　the　training　process　to　get　the　node　embedding.　This　may　bring　great　computational　cost　for　the　hyperspectral　data　with　a　large　number　of　pixels.　Therefore,　in　this　article,　we　propose　an　inductive　learning　method　to　solve　the　problem.　It　constructs　the　graph　by　sampling　and　aggregating　(GraphSAGE)　feature　from　a　node＇s　local　neighborhood.　This　could　greatly　reduce　the　space　complexity.　Moreover,　to　enhance　the　classification　performance,　we　also　construct　the　graph　using　spectral　and　spatial　information　(spectra-spatial　GraphSAGE).　Experiments　on　several　hyperspectral　image　datasets　show　that　the　proposed　method　can　achieve　better　classification　performance　compared　with　state-of-the-art　HSI　classification　methods.