Nonlinear　analyses　possess　tremendous　significance　throughout　the　entire　lifespans　of　civil　structures.　In　recent　years,　the　interest　in　leveraging　deep　learning　(DL)　to　address　the　efficiency　limitations　of　the　traditional　structural　analysis　methods　has　increased.　However,　full-range　nonlinear　analyses　of　different　structures　remain　underresearched　because　of　a　lack　of　appropriate　data　representations　and　the　failure　to　consider　both　internal　structural　information　and　external　load　conditions.　A　heterogeneous　graph　(HetG)　representation　scheme　that　can　digitalize　arbitrary　structural　systems　with　high　fidelity　is　proposed　in　this　study.　Furthermore,　a　composite　feature　learning　framework　is　developed　to　enable　efficient　full-range　nonlinear　analyses.　This　framework　comprises　two　main　components:　①　a　heterogeneous　graph　neural　network　(GNN)-based　module　that　encodes　static　features　into　embeddings　with　full　structural　semantics　and　②　a　sequence-to-sequence　(Seq2Seq)　module　that　predicts　history-dependent　responses　using　structural　embeddings　and　external　stimuli　in　an　end-to-end　manner.　A　computational　model　named　structural　analysis　based　on　a　graph　neural　network-nonlinear　(StructGNN-N)　is　implemented　based　on　the　proposed　methodology　and　is　validated　through　numerical　experiments　involving　real-world　concrete　structures.　The　results　show　that　StructGNN-N　successfully　reproduces　the　full-range　nonlinear　responses　of　all　nodes　in　the　entire　structure　and　exhibits　excellent　generalizability　across　structures　with　diverse　topological　designs　and　member　configurations.　Notably,　the　developed　model　achieves　a　computational　efficiency　level　that　is　1000　times　greater　than　that　of　the　traditional　elastoplastic　history　analysis　approach　using　the　finite-element　(FE)　method.　A　parametric　analysis　and　ablation　studies　demonstrate　the　effectiveness　of　the　StructGNN-N　architecture.　Due　to　its　superior　accuracy　and　computational　efficiency,　the　proposed　method　holds　great　potential　for　use　in　engineering　applications,　especially　in　the　context　of　digital　twins.　This　approach　provides　an　inspiring　path　for　simulating　diverse　engineering　structures　with　accurate　and　comprehensive　mechanical　information　in　real　time.　©　2025　THE　AUTHORS