Hyperspectral　unmixing　is　a　crucial　task　for　hyperspectral　images　(HSIs)　processing,　which　estimates　the　proportions　of　constituent　materials　of　a　mixed　pixel.　Usually,　the　mixed　pixels　can　be　approximated　using　a　linear　mixing　model.　Since　each　material　only　occurs　in　a　few　pixels　in　real　HSI,　sparse　nonnegative　matrix　factorization　(NMF),　and　its　extensions　are　widely　used　as　solutions.　Some　recent　works　assume　that　materials　are　distributed　in　certain　structures,　which　can　be　added　as　constraints　to　sparse　NMF　model.　However,　they　only　consider　the　spatial　distribution　within　a　local　neighborhood　and　define　the　distribution　structure　manually,　while　ignoring　the　real　distribution　of　materials　that　is　diverse　in　different　images.　In　this　article,　we　propose　a　new　unmixing　method　that　learns　a　subspace　structure　from　the　original　image　and　incorporate　it　into　the　sparse　NMF　framework　to　promote　unmixing　performance.　Based　on　the　self-representation　property　of　data　points　lying　in　the　same　subspace,　the　learned　subspace　structure　can　indicate　the　global　similar　graph　of　pixels　that　represents　the　real　distribution　of　materials.　Then　the　similar　graph　is　used　as　a　robust　global　spatial　prior　which　is　expected　to　be　maintained　in　the　decomposed　abundance　matrix.　The　experiments　conducted　on　both　simulated　and　real-world　HSI　datasets　demonstrate　the　superior　performance　of　our　proposed　method.