To address issues such as low prediction accuracy, poor real-time performance, and weak model generalization in lower limb joint angle prediction, this study proposes a method based on VMD-Informer and surface electromyography signals (sEMG). First, sEMG signals and corresponding joint angle data were collected from subjects during walking and stair-climbing patterns.To enhance the stability of raw data, the variational modal decomposition (VMD) algorithm was applied to decompose the EMG signals. The negative gradient and adaptive particle swarm optimization (NGSPSO) algorithms were used to optimize two key parameters of VMD: The number of Intrinsic modal function (IMF) components and the penalty factor. Next, multi-domain features were extracted from each IMF. Principal component analysis (PCA) was applied to identify key factors within the feature sequences, thereby reducing the model′s input dimension. Finally, the Informer model was employed for dynamic temporal modeling of the multivariate feature sequences. Experimental results demonstrate that the proposed VMD-Informer model achieves RMSE values of 2.688 5° and 3.351 6° for hip and knee joints, respectively, in flat walking scenarios， and 3.508 8° and 3.856 2° for the stair-climbing scenario, respectively. Compared to VMD-Transformer, this represents an 18% reduction in average error, significantly enhancing prediction accuracy and system real-time performance. This provides a technical foundation for recognizing movement intentions in rehabilitation exoskeletons.