Zero-shot　object　detection　aims　to　identify　objects　from　unseen　categories　not　present　during　training.　Existing　methods　rely　on　category　labels　to　create　pseudo-features　for　unseen　categories,　but　they　face　limitations　in　exploring　semantic　information　and　lack　robustness.　To　address　these　issues,　we　introduce　a　novel　framework,　EKZSD,　enhancing　zero-shot　object　detection　by　incorporating　external　knowledge　and　contrastive　paradigms.　This　framework　enriches　semantic　diversity,　enhancing　discriminative　ability　and　robustness.　Specifically,　we　introduce　a　novel　external　knowledge　extraction　module　that　leverages　attribute　and　relationship　prompts　to　enrich　semantic　information.　Moreover,　a　novel　external　knowledge　contrastive　learning　module　is　proposed　to　enhance　the　model＇s　discriminative　and　robust　capabilities　by　exploring　pseudo-　visual　features.　Additionally,　we　use　cycle　consistency　learning　to　align　generated　visual　features　with　original　semantic　features　and　adversarial　learning　to　align　visual　features　with　semantic　features.　Collaboratively　trained　with　contrast　learning　loss,　cycle　consistency　loss,　adversarial　learning　loss,　and　classification　loss,　our　framework　outperforms　superior　performance　on　the　MSCOCO　and　Ship-43　datasets,　as　demonstrated　in　experimental　results.