Aiming　at　that　the　granularity　of　the　predicate　feature　extracted　based　on　relation　bounding　box　is　relatively　coarse,　a　region-sensitive　scene　graph　generation　(RS-SGG)　method　is　proposed.　The　predicate　feature　extraction　module　divided　the　relationship　bounding　box　into　four　regions　and　used　the　self-attention　mechanism　to　suppress　background　regions　that　were　irrelevant　to　relationship　classification.　The　relationship　feature　decoder　comprehensively　employed　the　visual,　semantic　and　the　position　features　of　object　pairs　for　predicting　the　predicate　relationships.　Based　on　the　publicly　available　visual　genome　(VG)　dataset,　RS-SGG　was　compared　with　some　mainstream　scene　graph　generation　methods.　The　graph　constraint　recall　and　no　graph　constraint　recall　for　three　subtasks　including　scene　graph　detection,　scene　graph　classification,　and　predicate　classification　were　computed　to　evaluate　the　performance　of　the　SGG　models.　Results　show　that　graph　constraint　recall　and　no　graph　constraint　of　RS-SGG　are　better　than　that　of　the　mainstream　methods.　Additionally,　the　results　of　visualization　experiments　further　demonstrate　the　effectiveness　of　the　proposed　method.　©　2025　Beijing　University　of　Technology.　All　rights　reserved.