Human-Object　Interaction　(HOI)　detection　is　a　core　task　for　human-centric　visual　relationship　understanding.　However　most　existing　methods　still　face　the　challenge　of　accuracy　improvement.　Because　there　are　usually　too　many　human-object　pairs　and　complex　scenes　in　the　image,　which　leads　to　the　complexity　of　HOI　detection　task.　In　this　paper,　we　innovatively　introduce　a　multi-grained　interactive　intention　layer　to　deal　with　human　gaze　and　multi-level　human　pose　information　in　complex　human-object　scenes.　We　construct　a　hierarchical　architecture　using　macro-level　spatial　layout　cues,　mesco-level　interactive　intentions　and　micro-level　appearance　features　of　humans　and　objects　to　complement　the　various　levels　of　information.　Leading　results　on　two　public　benchmarks　validate　the　effectiveness　of　our　model.　　©　2023　IEEE.