The　existing　temporal　knowledge　graph　representation　methods　cannot　capture　the　complex　relationships　within　quadruple　well.　Most　of　the　neural　network　based　models　are　unable　to　model　time-varying　knowledge　and　capture　rich　feature　information.　Moreover,　the　interaction　between　entities　and　relations　in　these　models　is　poor.　Therefore,　a　multi-scale　dilated　convolutional　neural　network　model　based　on　attention　mechanism(MSDCA)　is　proposed.　Firstly,　a　time-aware　relation　representation　is　obtained　using　long　short-term　memory.　Secondly,　a　multi-scale　dilated　convolutional　neural　network　is　employed　to　improve　the　interactivity　of　the　quadruple.　Finally,　a　multi-scale　attention　mechanism　is　utilized　to　capture　critical　features　to　improve　completion　ability　of　MSDCA.　Link　prediction　experiments　on　multiple　public　temporal　datasets　show　the　superiority　of　MSDCA.　©　2021,　Science　Press.　All　right　reserved.