Existing　studies　on　medication　recommendation　are　mostly　based　on　the　electronic　health　records.　However,　these　data　are　difficult　to　reflect　the　current　health　status　of　patients　and　learn　the　current　health　needs　of　patients.　Thus,　this　paper　fuses　the　dialogue　and　disease　information,　and　proposes　a　dialogue-　based　structure　and　graph　attention　network　recommendation　algorithm.　Firstly,　the　grey　relational　analysis　and　graph　attention　network　are　integrated　and　the　relations　of　nodes　are　measured　using　grey　relational　analysis　to　provide　a　novel　relation-aware　graph　structure.　It　can　improve　the　traditional　graph　networks　to　learn　the　node　relations.　Secondly,　a　dialogue　hierarchical　encoder　is　established　to　encode　the　representations　of　utterance　and　dialogue　via　the　new　graph　structure.　Meanwhile,　two　graph　structures　are　designed　to　learn　the　node　correlation　for　generating　the　contextual　dialogue　representation.　Finally,　the　representations　of　disease　by　knowledge　graph　and　graph　network　are　incorporated　with　dialogue　to　predict　and　recommend.　Experimental　results　show　that　the　proposed　algorithm　is　superior　to　baselines　in　terms　of　all　metrics.　Compared　with　the　best　baseline　DNN,　the　performance　of　the　proposed　algorithm　improves　1.8%　and　3.5%　in　terms　of　F1　and　Jaccard,　which　shows　that　the　proposed　algorithm　can　improve　the　recommendation　performance.　©　2024　Journal　of　Computer　Engineering　and　Applications　Beijing　Co.,　Ltd.;　Science　Press.　All　rights　reserved.