Abstract: During the operation of an aircraft, the performance of certain systems or components gradually degrades, and both environmental factors and working conditions influence this degradation. Accurately predicting performance degradation by comprehensively accounting for these influences is a critical and complex task aimed at reducing failure-related losses. Moreover, as time progresses, the degradation trend may vary, which imposes higher requirements on the flexibility and adaptability of prediction models. First, this paper introduces the concept of shock effects and models the impact of working condition change using a Beta function while incorporating environmental variables to characterize environmental influences, thereby constructing a grey multivariate prediction model. Second, an optimization model is established to minimize the mean absolute percentage error (MAPE), and the Whale Optimization Algorithm is employed to estimate the unknown parameters related to the shock effects. Subsequently, a recursive least squares-based iterative mechanism is proposed to dynamically and flexibly update the model's structural parameters. Finally, empirical analysis is conducted using hydraulic pressure data from an aircraft hydraulic system under varying working conditions. The results demonstrate that the proposed model exhibits excellent predictive performance and practical value.