In　recent　years,　significant　progress　has　been　made　in　Multi-view　Subspace　Clustering　(MSC).　Most　existing　MSC　methods　attempt　to　explore　and　exploit　the　view　correlations　of　multi-view　data　to　boost　the　clustering　performance.　They　achieve　the　subspace　matrices　of　different　views　from　the　original　feature　space　directly.　However,　the　diversity　view-correlation　and　consistency-view　correlation　of　multi-view　data　are　two　antagonistic　properties,　which　are　improper　and　challenging　to　be　captured　in　such　a　straightforward　process.　To　simultaneously　and　properly　investigate　the　two　antagonistic　properties　of　multi-view　data,　a　novel　Double-level　View-correlation　Multi-view　Subspace　Clustering　method,　named　DV-MSC,　is　introduced　in　this　paper.　To　be　specific,　DV-MSC　adopts　a　strategy　that　deals　with　the　diversity　view-correlation　and　consistency　view-correlation　in　different　levels:　(1)　low-level,　which　excavates　the　diversity　view-correlation　in　the　feature　space,　and　(2)　high-level,　which　explores　the　consistency　view-correlation　in　subspace　representations.　The　underlying　assumption　is　that　different　views　should　be　diverse　in　the　feature　space　while　having　the　same　clustering　results,　in　other　words,　the　proposed　method　explores　the　Diversity　in　Low-level　Feature　Content　(DLFC)　and　the　Consistency　in　High-level　Clustering　Structure　(CHCS).　Experimental　results　show　the　promising　and　competitive　clustering　performance　of　DV-MSC,　compared　to　several　existing　state-of-the-arts.　©　2023　Elsevier　B.V.