Scene　graph　generation　(SGG)　aims　to　perceive　objects　and　their　relations　in　images,　which　can　bridge　the　gap　between　upstream　detection　tasks　and　downstream　high-level　visual　understanding　tasks.　For　SGG　models,　over-fitting　head　predicates　can　lead　to　bias　in　the　generated　scene　graph,　which　has　become　a　consensus.　A　series　of　debiasing　methods　have　been　proposed　to　solve　the　problem.　However,　some　existing　debiasing　SGG　methods　have　a　tendency　to　over-fit　tail　predicates,　which　is　another　type　of　bias.　In　order　to　eliminate　the　one-way　over-fitting　of　head　or　tail　predicates,　this　article　proposes　a　balanced　relation　prediction　(BRP)　module　which　is　model-agnostic　and　compatible　with　existing　re-balancing　methods.　Moreover,　because　the　relation　prediction　is　based　on　object　feature　representation,　this　article　proposes　a　scene　adaptive　context　fusion　(SACF)　module　to　refine　the　object　feature　representation.　Specifically,　SACF　models　the　context　based　on　a　chain　structure,　where　the　order　of　objects　in　the　chain　structure　is　adaptively　arranged　according　to　the　scene　content,　achieving　visual　information　fusion　that　adapts　to　the　scene　where　the　objects　are　located.　Experiments　on　VG　and　GQA　datasets　show　that　the　proposed　method　achieves　competitive　results　on　the　comprehensive　metric　of　R@K　and　mR@K.