Recently,　several　classifier　prediction　methods　have　emerged　exploiting　knowledge　graphs　and　Graph　Convolutional　Neural　Network　(GCN),　achieving　excellent　results　in　the　field　of　Zero-Shot　Learning　(ZSL).　However,　existing　methods　only　rely　on　pre-trained　seen　class　classifier　parameters　to　guide　the　model＇s　training,　prohibiting　the　discriminative　visual　features　from　being　mined　and　not　guaranteeing　the　effective　use　of　semantic　features.　Therefore,　this　work　presents　a　novel　Knowledge-Assisted　ZSL　Model　(KAZSLM),　which　improves　the　classification　ability　by　embedding　visual　information　and　semantic　information　into　the　classifier　space.　In　this　work,　GCN　classifier　prediction　network　promoted　by　word　embedding　and　inter-class　relationships　is　employed　as　the　Basic　Framework　(BF),　which　is　then　combined　with　a　Visual　Knowledge　Assistant　(VKA)　module　and　a　Semantic　Knowledge　Assistant　(SKA)　module　to　form　KAZSLM.　In　the　VKA　module,　the　average　visual　feature　of　all　the　samples　in　each　seen　class　and　its　corresponding　class　label　are　used　to　guide　the　model　to　refine　the　classifier　at　a　lower　computational　cost.　Regarding　the　SKA　module,　the　samples＇　semantic　features　per　class　are　applied　to　refine　the　classifier　through　a　GCN　with　a　loss　function　related　to　reconstruct　each　classes＇　semantic　features　from　the　corresponding　classifier　parameters.　These　two　assistant　modules　allow　visual　knowledge　and　semantic　knowledge　to　force　the　whole　model　to　acquire　more　precise　classifier.　Moreover,　a　simple　convolutional　residual　network　is　taken　to　further　reinforce　the　performance　of　the　model　on　the　AWA2　dataset.　Experimental　results　on　the　AWA2　and　ImageNet　datasets　demonstrate　that　KAZSLM　achieves　a　better　image　classification　performance　than　current　ZSL　classification　methods.