Accurately predicting remaining useful life (RUL) is crucial for ensuring the stable and reliable operation of large complex equipment. To enhance prediction accuracy while improving model robustness and generalization, a method based on dual-attention temporal convolutional networks and selective particle swarm optimization ensemble is proposed. Firstly, the dual-attention temporal convolutional networks are employed to explore the internal correlations between multi-category input features and different time steps in monitoring data, enhancing degradation information from both feature and temporal dimensions. Subsequently, the selective particle swarm optimization ensemble algorithm prunes underperforming base models at various time scales, autonomously generating an optimal subset of models and assigning optimal weights for weighted output predictions. The proposed method has been validated on a dataset of aviation turbofan engine degradation, demonstrating a 13.9% improvement in prediction accuracy compared to other methods.