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Fault diagnosis of five gas path parts such as fan, booster, high-pressure compressor(HPC), high-pressure turbine(HPT), low-pressure turbine(LPT)'s efficiency degradation have been conducted based on Radial-Basis Function(RBF) neural network. The training and testing samples have been generated by Gasturb software. Diagnose result showed RBF neural network has advantage of less-time-costing and high-precision. RBF neural network can not merely isolate the fault parts, also it can determinate the degradation of components performance. In some instances, the diagnostics result doesn't agree with testing sample, but it also is reasonable solution, because the mathematical model of aero-engine is so complicated that the mathematical equation have more than one reasonable solutions. A degradation of gross performance may be caused by several combinations of components performance degradation. With increasing amplitude of noise, precision of diagnostics became worse. Sensitivity coefficient of diagnostics-precision corrupted by noise is variable with different amplitude of noise.