The　relationship　between　objects　can　be　described　from　different　angles.　Although　multiple　kinds　of　relationships　make　the　connections　between　objects　complex,　they　bring　in　more　discriminative　information　for　the　clustering　tasks.　Therefore,　how　to　effectively　fuse　multiple　kinds　of　relationships　becomes　a　critical　problem.　In　this　paper,　we　propose　a　novel　Multi-graph　Convolutional　Clustering　Network　which　deeply　explores　the　feature　information　of　nodes　and　fuses　the　multiple　kinds　of　relationships　between　nodes.　Unlike　most　graph　convolutional　clustering　methods　that　only　exploit　the　single　graph　or　directly　fuse　multiple　graphs　into　a　unified　graph　before　the　graph　convolution　operation,　we　firstly　build　multiple　parallelled　graph　convolution　layers　for　each　graph　to　learn　diverse　data　representations,　which　fully　exploits　different　statistics　information　between　graphs.　Then,　a　designed　multi-graph　attention　module　fuses　above　data　representations　and　considers　the　importance　of　each　graph.　Besides,　the　proposed　model　completes　the　transition　from　single　graph　to　multiple　graphs,　which　reduces　the　dependence　of　the　quality　of　the　single　graph　and　enhances　the　robustness　to　graphs.　Experimental　results　verify　that　the　proposed　multi-graph　convolution　clustering　performs　better　than　the　traditional　single-graph　convolution　clustering.