Aspect-level　multimodal　sentiment　analysis　has　also　become　a　new　challenge　in　the　field　of　sentiment　analysis.　Although　there　has　been　significant　progress　in　the　task　based　on　image-　text　data,　existing　works　do　not　fully　deal　with　the　implicit　sentiment　expression　in　data.　In　addition,　they　do　not　fully　exploit　the　important　information　from　external　knowledge　and　image　tags.　To　address　these　problems,　we　propose　a　knowledge-augmented　heterogeneous　graph　convolutional　network　(KAHGCN).　First,　we　propose　a　dynamic　knowledge　selection　algorithm　to　select　the　most　relevant　external　knowledge,　thereby　enhancing　KAHGCN＇s　ability　of　understanding　the　implicit　sentiment　expression　in　review　texts.　Second,　we　propose　a　graph　construction　strategy　to　construct　a　heterogeneous　graph　that　aggregates　review　text,　image　tags　and　external　knowledge.　Third,　we　propose　a　multilayer　heterogeneous　graph　convolutional　network　to　strengthen　the　interaction　between　information　from　external　knowledge,　review　texts　and　image　tags.　Experimental　results　on　two　datasets　demonstrate　the　effectiveness　of　the　KAHGCN.