Actuators are essential components of flight control systems, and their performance directly affects flight safety. However, most components can only get the performance data 2~ 3 times during their life cycle, and the samples of performance degradation parameters are extremely small, which poses challenges for predicting actuator performance. To solve this problem, a performance prediction method combining statistical analysis and a physical model is proposed. Firstly, statistical analysis is carried out on the batch-type component data, and statistical distribution rules of different stages of the actuator are established. Then, based on the physical model and statistical law of the actuator degradation, a actuator degradation function with probability distribution is established. The function parameters are calibrated based on AMESim simulation to obtain the probability density function under different time and health parameters. Finally, the update method of probability density function based on posterior probability is given for the health parameters obtained by any component. To verify the effectiveness of the method, multiple samples containing 3 data points were used for validation. The results show that the probability of the measured values in the 3σ range of the predicted density function is 92. 27% , which proves that the predicted density function can characterize the degradation rule of actuators with high confidence.