Generalized　zero-shot　learning(GZSL)　aims　to　recognize　images　from　seen　and　unseen　classes　with　side　information,　such　as　manually　annotated　attribute　vectors.　Traditional　methods　focus　on　mapping　images　and　semantics　into　a　common　latent　space,　thus　achieving　the　visual-semantics　alignment.　Since　the　unseen　classes　are　unavailable　during　training,　there　is　a　serious　problem　of　recognition　bias,　which　will　tend　to　recognize　unseen　classes　as　seen　classes.　To　solve　this　problem,　we　propose　a　Domain-aware　Prototype　Network(DPN),　which　splits　the　GZSL　problem　into　the　seen　class　recognition　and　unseen　class　recognition　problem.　For　the　seen　classes,　we　design　a　domain-aware　prototype　learning　branch　with　a　dual　attention　feature　encoder　to　capture　the　essential　visual　information,　which　aims　to　recognize　the　seen　classes　and　discriminate　the　novel　categories.　To　further　recognize　the　fine-grained　unseen　classes,　a　visual-semantic　embedding　branch　is　designed,　which　aims　to　align　the　visual　and　semantic　information　for　unseen-class　recognition.　Through　the　multi-task　learning　of　the　prototype　learning　branch　and　visual-semantic　embedding　branch,　our　model　can　achieve　excellent　performance　on　three　popular　GZSL　datasets.