Structural health monitoring (SHM) of long-span bridges is frequently challenged by non-stationary behavior, superimposed multi-scale features, and significant noise interference. Traditional physics-based methods often struggle to accurately capture the complex data patterns to predict structural dynamic responses. To address these limitations, this study proposes a deep learning framework integrating adaptive signal decomposition with intelligent optimization for dynamic response prediction of long-span bridges. Specifically, an improved method based on variational modal decomposition and long short-term memory is developed to predict the structural responses of a steel-concrete composite rib-arch bridge. The SHM data were continuously collected from the arch bridge over a of 31 d period, with wind speed, equivalent vehicle load, and structural temperature designed as input variables to model key response indicators, including crown displacement, deflection, strain, cable force, and vibration acceleration. Adaptive signal decomposition facilitates multi-scale features and noise suppression, while intelligent optimization enhances hyperparameter tuning for time-series modeling. Numerical results demonstrate reasonable prediction accuracy for the key responses. The coefficient of determination (R²) for the predictions of strain, crown displacement and deflection reached 0.986, 0.968, and 0.975, respectively. Notably, for highly non-stationary vibration acceleration signals, the proposed framework significantly outperformed mainstream baseline models, reducing multiple error metrics to minimal levels. The proposed method has advantages in terms of prediction accuracy, stability, and generalization, which provides robust support for condition assessment and early safety warnings of long-span bridges.