Obtaining　the　off-design　characteristics　of　core　components　such　as　turbines　and　compressors　is　the　basis　of　off-design　analysis　for　energy　systems.　However,　the　characteristics　are　difficult　to　accurately　acquire　in　the　initial　design　stage　of　turbomachinery.　Based　on　deep　learning　techniques,　an　accurate　and　rapid　field　reconstruction　and　off-design　aerodynamic　performance　prediction　method　is　proposed.　First,　a　Generative　Adversarial　Network　with　the　added　Bezier　layer　is　employed　to　establish　a　database　of　turbine　blade　profiles.　Then,　a　Dual　Convolutional　Neural　Network　(Dual-CNN)　is　established　to　reconstruct　the　pressure　and　temperature　fields　as　well　as　predict　the　off-design　performances　of　different　profiles　and　working　conditions.　Based　on　the　above　two　kinds　of　neural　networks,　a　turbine　in　a　solar-based　supercritical　carbon　dioxide　Brayton　cycle　is　taken　as　an　example.　The　field　reconstruction　and　off-design　performance　prediction　are　conducted　on　the　basis　of　the　established　rotor　blade　profile　database.　The　accuracy　of　field　reconstruction　is　guaranteed.　The　off-design　performance　prediction　of　the　established　Dual-CNN　indicates　that　the　example　blade　profile　is　suitable　for　operation　with　larger　mass　flow　rate.　Compared　with　the　traditional　method,　Dual-CNN　can　reduce　the　off-design　analysis　time　of　one　blade　geometry　from　38.4　h　to　7.68s.　©　2021