Hyperspectral　Images　(HSIs)　contain　enriched　information　due　to　the　presence　of　various　bands,　which　have　gained　attention　for　the　past　few　decades.　However,　explosive　growth　in　HSIs＇　scale　and　dimensions　causes　＂Curse　of　dimensionality＂　and　＂Hughes　phenomenon＂.　Dimensionality　reduction　has　become　an　important　means　to　overcome　the　＂Curse　of　dimensionality＂.　In　hyperspectral　images,　labeled　samples　are　more　difficult　to　collect　because　they　require　many　labor　and　material　resources.　Semi-supervised　dimensionality　reduction　is　very　important　in　mining　high-dimensional　data　due　to　the　lack　of　costly-labeled　samples.　The　promotion　of　the　supervised　dimensionality　reduction　method　to　the　semi-supervised　method　is　mostly　done　by　graph,　which　is　a　powerful　tool　for　characterizing　data　relationships　and　manifold　exploration.　To　take　advantage　of　the　spatial　information　of　data,　we　put　forward　a　novel　graph　construction　method　for　semi-supervised　learning,　called　SLIC　Superpixel-based　l(2,1)-norm　Robust　Principal　Component　Analysis　(SURPCA(2,1)),　which　integrates　superpixel　segmentation　method　Simple　Linear　Iterative　Clustering　(SLIC)　into　Low-rank　Decomposition.　First,　the　SLIC　algorithm　is　adopted　to　obtain　the　spatial　homogeneous　regions　of　HSI.　Then,　the　l(2,1)-norm　RPCA　is　exploited　in　each　superpixel　area,　which　captures　the　global　information　of　homogeneous　regions　and　preserves　spectral　subspace　segmentation　of　HSIs　very　well.　Therefore,　we　have　explored　the　spatial　and　spectral　information　of　hyperspectral　image　simultaneously　by　combining　superpixel　segmentation　with　RPCA.　Finally,　a　semi-supervised　dimensionality　reduction　framework　based　on　SURPCA(2,1)　graph　is　used　for　feature　extraction　task.　Extensive　experiments　on　multiple　HSIs　showed　that　the　proposed　spectral-spatial　SURPCA(2,1)　is　always　comparable　to　other　compared　graphs　with　few　labeled　samples.