Safety and reliability are crucial for the next-generation supercapacitors used in energy storage systems, while accurate prediction of the degradation trajectory and remaining useful life (RUL) is essential for analyzing degradation and evaluating performance in energy storage systems. This study proposes a novel data processing and improved one-dimensional convolutional neural network (1D CNN)-informer framework for robust RUL prediction. In data preprocessing, all data from two structures are adjusted to a unified format, and cross-entropy loss is used to couple the 1D CNN and informer. Then, the minimum-maximum feature scaling method is used for normalization to accelerate the training process in reaching the minimum cost function. A relative position encoding algorithm is introduced to improve the Informer model, enabling it to better learn the sequence relationships between data and effectively reduce prediction variability. Supercapacitor data in different working conditions are used to validate the proposed method. Compared with other existing methods, the maximum root mean square error is reduced by 32.71%, the mean absolute error is reduced by 28.50%, and R2 is increased by 4.79%. The strategy considers the complementarity between two single models, which can extract features and enrich local details, as well as enhance the model's global perception ability. The experimental results demonstrate that the proposed model achieves high-precision and robust RUL prediction, thereby promoting the industrial application of supercapacitors.