In　recent　years,　the　appearance　of　graph　convolutional　networks　(GCNs)　provides　a　new　idea　for　graph　structure　data　processing.　Because　of　that,　they　can　learn　excellent　user　and　item　embedding　by　using　cooperative　signals　of　high-order　neighbors,　and　the　GCNs　technique　shows　great　potential　in　the　recommendation.　The　common　problem　with　the　bulk　of　GCN-based　models　is　that　it　appears　the　situation　of　performance　degradation　during　the　stacking　of　network　layers.　The　recently　proposed　IMP-GCN　alleviates　this　problem　to　some　extent.　It　aims　to　avoid　the　influence　of　downside　information　from　high-order　propagation　on　embedding　learning.　However,　we　consider　that　it　ignores　the　multi-interest　factor,　in　which　users　may　have　different　interests.　In　this　paper,　we　present　a　multi-interest　GCN(MI-GCN)　model　for　a　recommendation,　and　it　conducts　high-order　graph　convolution　operations　in　three　sets　of　subgraphs.　Users　with　similar　interests　and　the　corresponding　interaction　items　belong　to　the　identical　subgraph.　As　for　the　formation　of　the　subgraph,　we　adopt　two　varied　clustering　methods　and　the　user　feature　to　form　a　subgraph　generation　mechanism.　This　mechanism　can　generate　three　groups　of　differential　subgraphs　to　divide　users　into　multi-interest　groups　and　make　subgraph　division　more　reasonable.　We　carry　out　massive　experiments　on　three　real-world　datasets,　demonstrating　the　effectiveness　of　our　model.　Experimental　results　confirm　that　our　presented　MI-GCN　outperforms　the　state-of-the-art　GCN-based　recommendation　models.