The　research　on　the　supercritical　carbon　dioxide　(S-CO2)　Brayton　cycle　has　gradually　become　a　hot　spot　in　recent　years.　The　off-design　performance　of　turbine　is　an　important　reference　for　analyzing　the　variable　operating　conditions　of　the　cycle.　With　the　development　of　deep　learning　technology,　the　research　of　surrogate　models　based　on　neural　network　has　received　extensive　attention.　In　order　to　improve　the　inefficiency　in　traditional　off-design　analyses,　this　research　establishes　a　data-driven　deep　learning　off-design　aerodynamic　prediction　model　for　a　S-CO2　centrifugal　turbine,　which　is　based　on　a　deep　convolutional　neural　network.　The　network　can　rapidly　and　adaptively　provide　dynamic　aerodynamic　performance　prediction　results　for　varying　blade　profiles　and　operating　conditions.　Meanwhile,　it　can　illustrate　the　mechanism　based　on　the　field　reconstruction　results　for　the　generated　aerodynamic　performance.　The　training　results　show　that　the　off-design　aerodynamic　prediction　convolutional　neural　network　(OAP-CNN)　has　reduced　the　mean　and　maximum　error　of　efficiency　prediction　compared　with　the　traditional　Gaussian　Process　Regression　(GPR)　and　Artificial　Neural　Network　(ANN).　Aiming　at　the　off-design　conditions,　the　pressure　and　temperature　distributions　with　acceptable　error　can　be　obtained　without　a　CFD　calculation.　Besides,　the　influence　of　off-design　parameters　on　the　efficiency　and　power　can　be　conveniently　acquired,　thus　providing　the　reference　for　an　optimized　operation　strategy.　Analyzing　the　sensitivity　of　AOP-CNN　to　training　data　set　size,　the　prediction　accuracy　is　acceptable　when　the　percentage　of　training　samples　exceeds　50%.　The　minimum　error　appears　when　the　training　data　set　size　is　0.8.　The　mean　and　maximum　errors　are　respectively　1.46%　and　6.42%.　In　summary,　this　research　provides　a　precise　and　fast　aerodynamic　performance　prediction　model　in　the　analyses　of　off-design　conditions　for　S-CO2　turbomachinery　and　Brayton　cycle.　Copyright　©　2021　by　ASME.