In　most　learning　models　for　knowledge　graph　representation,　only　structural　knowledge　between　entities　and　relations　is　taken　into　account.　Therefore,　the　capability　of　the　models　is　limited　by　knowledge　storage,　and　the　completion　performance　of　knowledge　base　is　unstable.　Existing　knowledge　representation　methods　incorporating　external　information　mostly　model　for　a　specific　kind　of　external　modal　information,　leading　to　limited　application　scopes.　Thus,　a　knowledge　representation　learning　model,　Conv-AT,　is　proposed.　Firstly,　two　external　modes　of　information,　text　and　images,　are　considered,　and　three　schemes　fusing　external　knowledge　and　entities　are　introduced　to　obtain　multimodal　representation　of　entities.　Secondly,　the　performance　of　convolution　is　enhanced　and　the　quality　of　knowledge　representation　as　well　as　the　completion　ability　of　the　model　are　improved　by　combining　the　channel　attention　module　and　spatial　attention　module.　Link　prediction　and　triple　classification　experiments　are　conducted　on　public　multimodal　datasets,　and　the　results　show　that　the　proposed　method　is　superior　to　other　methods.　©　2021,　Science　Press.　All　right　reserved.