Medication　combination　prediction　can　be　applied　to　the　clinical　treatment　for　critical　patients　with　multi-morbidity.　The　suitable　medication　combination　can　help　cure　patients　and　keep　the　treatment　medication　safe.　However,　the　complexity　and　uncertainty　of　clinical　circumstances　limit　the　predictive　accuracy　of　medication　combination.　Thus,　this　paper　proposes　a　new　medication　combination　prediction　model　based　on　the　temporal　attention　mechanism　(TAM)　and　the　simple　graph　convolution　(SGC),　named　as　TAMSGC.　More　specifically,　the　TAM　can　capture　the　temporal　sequence　information　in　the　medical　records,　and　the　SGC　is　implemented　to　acquire　the　medication　knowledge　from　the　complicated　medication　combination.　Experiments　in　a　real　dataset　show　that　TAMSGC　surpasses　the　baseline　models　on　the　predictive　accuracy　of　medication　combination.