The degradation of power converter performance is one of the most critical issues of complex system with the improvement of power capacity and density. Power converter bears severe electrical and thermal stress, resulting in an increase in the probability of failure and significant economic losses. Most research addresses performance evaluation either through reliability theory without physical understanding or through data-driven methods requiring high experimental cost. Few studies focus on predicting system-level performance degradation, which is technically difficult as many components degrade randomly. Identifying the parameters of electronic components based on sensor data has become possible with the development of neural networks and computational power. Therefore, in this article, we propose a novel system-level power degradation predicting framework, which combines the advantages of neural networks in nonlinear fitting and empirical knowledge to predict the degradation of the power converter. In addition, a comprehensive and improved feature parameter screening method is proposed to identify the most critical feature parameters of the power converter systems. Furthermore, the neural network parameter identification method based on the sparrow search algorithm–Elman neural network is introduced to improve prediction accuracy. Finally, the result shows that the proposed method can accurately predict the degradation of the system by using a DC–DC converter as an example.