In　view　of　the　inability　of　deep　multi-view　subspace　clustering　network　to　distinguish　the　reliability　of　each　view　when　data　fusion,　and　the　lack　of　utilization　of　the　consistent　and　complementary　information　between　multi-view　data,　a　deep　multi-view　subspace　clustering　method　based　on　adaptive　weight　fusion　(DMSC-AWF)　was　proposed.　First,　a　common　representation　matrix　was　studied　by　making　each　view　of　sharing　the　same　self-representation　layer,　and　a　self-representation　layer　was　built　for　each　visual　to　learn　a　specific　representation　matrix.　The　efficient　use　of　consistent　and　complementary　information　that　depends　on　the　data　was　ensured.　Second,　the　importance　of　different　views　was　quantified　by　introducing　attention　modules　based　on　the　shared　self-representation　layer,　which　adaptively　assigned　weights　to　each　visual　data.　Finally,　clustering　experiments　were　conducted　on　four　public　datasets,　and　the　clustering　results　of　this　method　were　significantly　improved　compared　with　the　comparison　method.　Moreover,　the　validity　and　importance　of　the　attention　module　learning　visual　weight　were　verified　by　the　degradation　experiment.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.