Aerodynamic　design　optimization　of　the　blade　profile　is　a　critical　approach　to　improve　performance　of　turbomachinery.　This　paper　aims　to　achieve　the　performance　prediction　with　deep　learning　method　and　realize　fast　design　optimization　of　a　turbine　blade.　Two　parameterization　methods　based　on　geometric　relationships　(PGR)　and　neural　network　(PNN)　are　proposed,　which　can　generate　smooth　and　complete　blade　profiles.　A　dual　convolutional　neural　network　(DCNN)　is　constructed　to　predict　the　physical　fields　and　aerodynamic　performance.　The　implementations　of　DCNN　are　accomplished　based　on　the　datasets　generated　by　the　two　parameterization　methods　respectively,　which　are　called　PGR-DCNN　and　PNN-DCNN　model.　Results　show　that　the　prediction　accuracy　increases　and　then　keeps　stable　as　train　size　increases.　The　two　models　can　offer　the　detailed　physical　field　distribution　within　3　ms　and　accurately　predict　the　aerodynamic　performance.　The　prediction　errors　of　performance　parameters　for　99%　samples　in　validation　set　are　less　than　0.5%　with　PGR-DCNN　model,　which　are　significantly　better　than　conventional　machine　learning　methods.　Finally,　based　on　the　accurate　predictive　models,　the　gradient-based　design　optimization　for　rotor　blade　profile　is　completed　in　38　s.　The　efficiency　of　the　two　optimal　blades　reaches　89.29%　and　88.92%　respectively,　which　verifies　the　feasibility　of　our　method.　©　2022　Elsevier　Ltd