To　improve　the　efficiency　of　the　multidisciplinary　design　optimization　of　underwater　vehicles,　this　paper　proposes　a　combined　proxy　model　with　adaptive　dynamic　sampling.　The　radial　basis　function　model　(RBF),　Kriging　model,　and　polynomial　response　surface　model　(PRS)　are　used　to　construct　the　proxy　model.　Efficient　sample　points　are　collected　based　on　the　synthetic　minority　oversampling　technique　(SMOTE)　algorithm　and　the　lower　confidence　bound　(LCB)　criterion.　The　proxy　model　process　is　integrated　after　dynamic　sampling.　The　collaborative　optimization　framework　is　used,　which　considers　the　coupling　between　the　main　system　set　and　the　subsystem　set.　The　hierarchical　analysis　method　is　used　to　transform　the　multidisciplinary　optimization　problem　into　a　single-objective　optimization　problem.　Computational　fluid　dynamics　(CFD)　numerical　simulation　is　utilized　to　simulate　underwater　submarine　navigation.　The　optimization　strategy　is　applied　to　the　underwater　vehicle　SUBOFF　to　optimize　resistance　and　energy　consumption.　Three　dynamic　proxy　models　and　three　static　proxy　models　are　compared.　The　results　show　that　the　optimization　efficiency　of　the　underwater　vehicle　has　been　improved.　To　prove　the　generalization　performance　of　the　proposed　combined　proxy　model,　a　reducer　example　is　investigated　for　comparison.　The　results　show　that　the　combined　proxy　model　(CPM)　is　highly　accurate　and　has　excellent　generalization　performance.