The on-wing monitoring data of aircraft auxiliary power unit (APU) are difficult to characterize its performance states, which will lead to the difficulty that the performance evaluation and remaining useful life (RUL) prediction of the APU is difficult to carry out. To solve this problem, a performance evaluation and RUL prediction approach of APU is proposed based on the state space model ( SSM) and Kalman filter (KF). Firstly, a performance indicator (PI) containing noise is constructed from the on-wing monitoring data to characterize the performance state of the APU. The performance degradation process of APU is described by state equation, which is constructed with the help of the Wiener process and the constructed PI with noise contained. Then, the KF state estimation and prediction method is applied to the SSM. Through estimating the on-wing performance state of APU, the purpose of predicting RUL is achieved. Finally, the APU on-wing monitoring data from the operation of an airline company in China are adopted to conduct the comprehensive verification and evaluation of the proposed method. Experiment results show that compared with ELM and Optimized ELM, the prediction absolute percentage error of the proposed method is reduced by 72. 1% and 67. 9% , respectively. In addition, compared with the experiment results of other three kinds of methods, the prediction absolute percentage error of the proposed method is reduced by 69. 2% at least. The proposed method can effectively predict the RUL of an on-wing APU, which can provide a reference for the operation and maintenance personnel to plan maintenance and repair reasonably. More importantly, the method can improve the comfort of the passengers and the aircraft safety to a certain degree.