An optimized relevance vector machine（RVM） life prediction method is proposed for the remaining life of auxiliary power unit （APU）. Firstly， an improved kernel function is proposed by taking into account both efficiency and accuracy. Furthermore， the beetle antennae search （BAS） algorithm is applied to optimize the kernel parameters of RVM. Secondly， through analyzing the historical A13 message and maintenance record data of an airline's APU， the exhaust gas temperature （EGT） parameters are extracted， corrected， and noise reduced， and the turbine performance degradation pattern library of EGT is established with polynomial regression. Finally， it is proved that compared with the traditional RVM， the efficiency and accuracy of the proposed algorithm in the APU turbine life prediction are improved by 40%， 20%， respectively. In addition， the optimal initial step size and input dimension in the model are determined based on sensitivity analysis.