The　development　of　big　data　has　brought　changes　to　society　and　brought　us　challenges.　How　to　extract　useful　information　from　complex　data　has　become　the　focus　of　research　in　recent　years.　Personalized　recommendation　as　an　effective　solution　has　received　widespread　attention　in　academia　and　industry.　Collaborative　filtering　has　been　widely　used　by　finding　users　with　similar　user　behaviors　as　the　preferences　of　similar　users.　However,　the　existing　methods　ignore　the　interaction　information　between　the　user　and　the　item　during　feature　extraction,　which　leads　to　imperfect　feature　extraction　and　affects　the　algorithm　effect.　This　paper　proposes　a　graph　convolution　collaborative　filtering　model　based　on　the　attention　model,　which　uses　the　graph　convolution　network　to　embed　user　and　item　interaction　information　into　feature　vectors,　and　uses　the　attention　model　to　highlight　the　relatively　important　interaction　information　among　them,　so　as　to　obtain　more　excellent　feature　vector.　The　experimental　results　show　that　the　model　has　a　good　effect　on　the　two　commonly　used　evaluation　metrics:　recall　and　normalized　discounted　cumulative　gain(NDCG).　©　2020　ACM.