Learning　to　recognize　unseen　objects　is　the　goal　of　zero-shot　learning　(ZSL),　building　on　transferring　the　class-level　semantic　descriptions.　Previous　methods　devote　to　bridging　the　instance-level　objects　with　classlevel　semantics　through　feature　generation　or　co-embedding,　neglecting　prototype-level　and　distribution-level　associations,　which　is　not　conducive　to　narrowing　the　visual-semantic　gap.　This　paper　yields　a　novel　prototype　rectification　framework　for　ZSL,　termed　PRZSL,　which　is　dedicated　to　learning　and　calibrating　the　dual　prototype　distributions　in　a　meta-domain.　We　first　propose　a　contrastive　embedding　module　with　a　compatibility　loss　and　an　angular　loss　to　make　inter-class　prototypes　well-separated.　We　further　collaboratively　rectify　the　dual　prototypes　by　injecting　the　prototype　distribution　information　of　another　modality,　boosting　the　visual-semantic　alignment　at　the　distribution　level.　Unlike　previous　methods　that　anchor　the　semantic　position,　semantic　prototypes　also　participate　in　collaborative　updates,　thereby　promoting　alignment　from　semantic　to　vision.　Comprehensive　experimental　results　on　five　zero-shot　benchmarks　demonstrate　that　our　proposed　method　can　achieve　competitive　performance　compared　with　the　state-of-the-art　methods.