Hyperspectral　unmixing　is　one　of　the　most　important　techniques　in　the　remote　sensing　image　analysis.　In　recent　years,　the　nonnegative　matrix　factorization　(NMF)　method　is　widely　used　in　hyperspectral　unmixing.　In　order　to　solve　the　nonconvex　problem　of　the　NMF　method,　a　number　of　constraints　have　been　introduced　into　NMF　models,　including　sparsity,　manifold,　smoothness,　etc.　However,　these　constraints　ignore　an　important　property　of　a　hyperspectral　image,　i.e.,　the　spectral　responses　in　a　homogeneous　region　are　similar　at　each　pixel　but　vary　in　different　homogeneous　regions.　In　this　paper,　we　introduce　a　novel　region-based　structure　preserving　NMF　(R-NMF)　to　explore　consistent　data　distribution　in　the　same　region　while　discriminating　different　data　structures　across　regions　in　the　unmixed　data.　In　this　method,　a　graph　cut　algorithm　is　first　applied　to　segment　the　hyperspectral　image　to　small　homogeneous　regions.　Then,　two　constraints　are　applied　to　the　unmixing　model,　which　preserve　the　structural　consistency　within　the　region　while　discriminating　the　differences　between　regions.　Results　on　both　synthetic　and　real　data　have　validated　the　effectiveness　of　this　method,　and　shown　that　it　has　outperformed　several　state-of-the-art　unmixing　approaches.