Existing　multi-view　deep　subspace　clustering　methods　aim　to　learn　a　unified　representation　from　multi-　view　data,　while　the　learned　representation　is　difficult　to　maintain　the　underlying　structure　hidden　in　the　origin　samples,　especially　the　high-order　neighbor　relationship　between　samples.　To　overcome　the　above　challenges,　this　paper　proposes　a　novel　multi-order　neighborhood　fusion　based　multi-view　deep　subspace　clustering　model.　We　creatively　integrate　the　multi-order　proximity　graph　structures　of　different　views　into　the　self-expressive　layer　by　a　multi-order　neighborhood　fusion　module.　By　this　design,　the　multi-order　Laplacian　matrix　supervises　the　learning　of　the　view-consistent　self-　representation　affinity　matrix;　then,　we　can　obtain　an　optimal　global　affinity　matrix　where　each　connected　node　belongs　to　one　cluster.　In　addition,　the　discriminative　constraint　between　views　is　designed　to　further　improve　the　clustering　performance.　A　range　of　experiments　on　six　public　datasets　demonstrates　that　the　method　performs　better　than　other　advanced　multi-view　clustering　methods.　The　code　is　available　at　https://github.com/songzuolong/MNF-MDSC　(accessed　on　25　December　2024).