Scene　graph　generation　provides　high-order　semantic　information　by　understanding　the　objects　and　their　relations　in　images.　In　order　to　improve　the　performance　of　scene　graph　generation,　context　fusion　has　been　widely　used　in　scene　graph　generation　tasks,　LSTM　and　Vision-Transformer　are　commonly　used　fusion　modules.　Both　LSTM　and　Vision-Transformer　realize　context　fusion　by　stacking　multiple　basic　units,　which　needs　to　learn　a　large　number　of　parameters　of　the　units.　However,　the　model　computational　efficiency　of　scene　graph　generation　as　a　mid-level　semantic　understanding　task　to　support　downstream　tasks　is　crucial.　To　simplify　the　context　fusion　computation,　this　paper　proposes　ASCF　-Net　(Augmented　Spatial　Context　Fusion　Network)　which　computes　the　spatial　context　of　designated　object　by　searching　the　nearest　neighbor　objects　with　high　relevance　and　strengthens　the　context　with　random　noise.　Without　learning　parameters,　the　above　computational　process　essentially　simulates　the　attention　mechanism.　Experiments　on　VG　dataset　show　that　ASCF　-Net　uses　15.26%　of　the　parameters　of　Bi-LSTM　and　13.34%　of　the　parameters　of　Vision-Transformer　for　context　fusion　based　on　the　same　baseline　and　achieves　higher　performance　than　using　the　two　fusion　modules.　At　the　same　time,　ASCF　-Net　uses　simple　fusion　module　to　obtain　competitive　results　on　VG　dataset　compared　with　the　mainstream　scene　generation　models.　©　2023　IEEE.