Multi-view　clustering　can　explore　consistent　information　from　different　views　to　guide　clustering.　Most　existing　works　focus　on　pursuing　shallow　consistency　in　the　feature　space　and　integrating　the　information　of　multiple　views　into　a　unified　representation　for　clustering.　These　methods　did　not　fully　consider　and　explore　the　consistency　in　the　semantic　space.　To　address　this　issue,　we　proposed　a　novel　Multi-level　Consistency　Collaborative　learning　framework　(MCoCo)　for　multi-view　clustering.　Specifically,　MCoCo　jointly　learns　cluster　assignments　of　multiple　views　in　feature　space　and　aligns　semantic　labels　of　different　views　in　semantic　space　by　contrastive　learning.　Further,　we　designed　a　multi-level　consistency　collaboration　strategy,　which　utilizes　the　consistent　information　of　semantic　space　as　a　self-supervised　signal　to　collaborate　with　the　cluster　assignments　in　feature　space.　Thus,　different　levels　of　spaces　collaborate　with　each　other　while　achieving　their　own　consistency　goals,　which　makes　MCoCo　fully　mine　the　consistent　information　of　different　views　without　fusion.　Compared　with　state-of-the-art　methods,　extensive　experiments　demonstrate　the　effectiveness　and　superiority　of　our　method.　Our　code　is　released　on　https://github.com/YiyangZhou/MCoCo.