In　recent　years,　drug　recommendation　based　on　deep　learning　models　has　been　extensively　studied　and　widely　applied　in　the　field　of　wisdom　medicine.　This　paper　proposes　a　drug　recommendation　model　with　dual　graph　convolutional　network　based　on　graph　embedding.　Firstly,　it　constructs　the　knowledge　graph　of　patient＇s　attributes　and　medications.　The　embedding　representation　is　obtained　using　graph　embedding.　Secondly,　the　embedding　representation　of　the　knowledge　graph　of　patient＇s　attributes　are　put　into　the　multilevel　graph　attention　network　layer　loaded　with　attention　mechanism　and　bidirectional　propagation　mechanism　for　disseminating　and　aggregating　information.　Then,　the　representation　of　patient＇s　attributes　and　the　embedded　representation　of　the　knowledge　graph　of　patient＇s　medications　are　integrated.　They　are　put　into　the　multilevel　graph　attention　network　layer　training　again　to　mine　the　high-level　association　between　patient＇s　attributes　and　medications.　Finally,　the　drug　recommendation　is　completed.　It　carries　out　an　empirical　study　with　the　basic　patient＇s　information,　physiological　characteristics　and　patient＇s　medication　data　in　the　data　set　of　the　medical　information　mart　for　intensive　care　Ⅳ　as　the　objects.　The　experimental　results　prove　that　it　outperforms　the　baseline　method　in　four　evaluation　indexes:precision,　recall,　F1　score　and　NDCG.　©　2024　Journal　of　Computer　Engineering　and　Applications　Beijing　Co.,　Ltd.;　Science　Press.　All　rights　reserved.