Graph　contrastive　learning　methods　often　construct　numerous　homogenous　graphs　by　randomly　adding　edge　perturbance　and　removing　edges　to/from　one　given　graph,　while　it　is　difficult　to　guarantee　the　quality　of　these　augmented　graph　structures.　To　fully　guarantee　and　utilize　the　rich　structural　information　provided　by　the　above　heterogeneous　graphs,　we　propose　a　novel　multi-graph　contrastive　clustering　network　model　that　collaborates　various　types　of　relationships　between　nodes.　In　contrast　to　traditional　contrastive　learning　methods　that　only　regard　each　node　as　one　positive　sample　and　its　neighbors　as　negative　samples　within　a　single　graph,　we　further　build　the　contrastive　constraint　between　nodes　of　the　same　sample　in　different　graphs,　improving　the　node　representation　capability　from　the　topology　and　sample　attribute　aspects.　The　designed　multi-graph　attention　mechanism　assigns　more　weights　to　significant　graphs　and　nodes.　Experimental　results　on　three　public　multi-graph　datasets　demonstrate　that　the　proposed　method　achieves　satisfactory　performance　compared　to　other　state-of-the-art　clustering　methods.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.