Zero-shot　learning　is　one　of　the　most　challenging　machine　learning　tasks,　in　which　learning　stable　and　transferable　knowledge　from　seen　classes　plays　a　pivotal　role.　To　improve　the　currently　unsatisfactory　performance　of　zero-shot　object　recognition,　this　paper　proposes　a　novel　image　representation　method,　namely,　micro-knowledge.　In　our　method,　the　segmentation　of　microregions　and　the　consequent　learning　of　micro-knowledge　are　unified　by　the　introduction　of　a　self-attention　mechanism.　A　zero-shot　classification　framework　is　carefully　designed　based　on　micro-knowledge　of　images.　Under　this　framework,　multiple　micro-region　descriptions　are　first　obtained　by　embedding　micro-knowledge　and　then　merged　to　carry　out　the　final　classification　of　unseen　objects.　Finally,　a　capsule-unified　framework　is　employed　as　a　graphical　programming　tool　to　accomplish　the　aforementioned　tasks.　Experiments　on　public　datasets　show　that　the　proposed　framework　can　generally　achieve　competitive　results　for　the　classification　of　unseen　objects.　Specifically,　these　results　verify　that　the　micro-knowledge　learned　from　one　dataset　can　be　directly　applied　to　others　without　complicated　adjustments　and　demonstrate　that　using　visual　features　instead　of　semantic　features　can　result　in　a　decrease　in　classification　error.　This　research　will　bring　new　ideas　into　the　field　of　zero-shot　learning　and　will　serve　as　an　appealing　option　when　addressing　the　problem　of　domain　shift.