Zero-shot　learning　(ZSL)　aims　to　recognize　images　from　unseen　(novel)　classes　with　the　training　images　from　seen　classes.　The　attributes　of　each　class　is　exploited　as　auxiliary　semantic　information.　Recently　most　ZSL　approaches　focus　on　learning　visual-semantic　embeddings　to　transfer　knowledge　from　the　seen　classes　to　the　unseen　classes.　However,　few　works　study　whether　the　auxiliary　semantic　information　in　the　class-level　is　extensive　enough　or　not　for　the　ZSL　task.　To　tackle　such　problem,　we　propose　a　hierarchical　coupled　dictionary　learning　(HCDL)　approach　to　hierarchically　align　the　visual-semantic　structures　in　both　the　class-level　and　the　image-level.　Firstly,　the　class-level　coupled　dictionary　is　trained　to　establish　a　basic　connection　between　visual　space　and　semantic　space.　Then,　the　image　attributes　are　generated　based　on　the　basic　connection.　Finally,　the　fine-grained　information　can　be　embedded　by　training　the　image-level　coupled　dictionary.　Zero-shot　recognition　is　performed　in　multiple　spaces　by　searching　the　nearest　neighbor　class　of　the　unseen　image.　Experiments　on　two　widely　used　benchmark　datasets　show　the　effectiveness　of　the　proposed　approach.　©　2021　ACM.