Object-object　affordance　recognition　aims　to　recognize　the　interactive　relations　between　an　object　and　other　objects,　which　plays　a　crucial　role　in　task　decision-making　and　object　selection　of　industrial　robots.　To　address　the　problem　of　interference　from　complex　interaction　relations,　we　propose　to　recognize　object-object　affordances　via　relational　phrase　learning.　The　relational　phrases　are　used　as　knowledge　prior　to　improve　the　affordance　expression.　In　addition,　we　propose　a　multi-scale　feature　pooling　and　aggregation　module　to　enhance　the　visual　feature　representation　of　images.　We　redesign　the　initial　block　of　transformer　decoder　to　model　the　visual　and　phrase　semantic　features,　improving　the　recognition　performance.　We　collect　and　annotate　object-object　interaction　images　from　the　robot＇s　view,　and　train　and　test　our　model　on　them.　The　experimental　results　show　that　our　approach　achieves　the　best　performance　compared　to　the　state-of-the-art　methods.　©　2023　IEEE.