Human-Object　Interaction　(HOI)　detection　aims　to　localize　human-object　pairs　and　comprehend　their　interactions.　Recently,　pre-trained　Vision-Language　Models　(VLM)　have　shown　their　great　recognition　ability　in　HOI　detection　task.　However,　these　VLM　based　methods　are　struggle　to　transfer　knowledge　to　achieve　desired　performance.　To　this　end,　we　propose　a　Dual-Branch　Knowledge　Enhancement　Network　with　VLM　(DBKEN-VLM)　within　the　two-stage　paradigm　to　enhance　the　effectiveness　of　VLM.　Specifically,　we　propose　a　semantic　mining　decoder　to　supplement　contextual　and　action-related　semantic　information　into　our　model.　It　forms　a　dual-branch　knowledge　enhancement　network　with　spatial　guided　decoder.　Furthermore,　we　propose　a　two-level　fusion　strategy　for　the　dualbranch　network　to　facilitate　better　knowledge　transfer　of　VLM.　One　is　feature-level　fusion,　producing　more　instructive　interaction　features;　another　is　decision-level　fusion,　further　enhancing　the　capability　of　VLM　for　HOI　detection.　The　proposed　method　achieves　competitive　performance　compared　to　recent　methods　on　two　benchmark　datasets,　HICO-DET　and　V-COCO.　©　2024　IEEE.