This paper proposes a novel framework for skeleton action prediction that integrates dynamic modeling, multi-scale feature learning, and noise modeling to address the challenges of predicting actions from partially observed and noisy data. Traditional methods struggle with limited observation data and noise, while skeleton-based action prediction has gained attention for its robustness to environmental changes and compact representation of human movements. To this end, we design a temporal Diffusion model that handles the uncertainty in partial observation data through an iterative denoising process and introduce a spatio-temporal adaptive attention Transformer to capture complex spatio-temporal relationships in skeleton sequences. Additionally, we propose mechanisms for dynamically adjusting time steps and non-uniform noise scheduling, enabling the model to adaptively learn noise characteristics across different temporal scales. To further enhance the model’s generalization and prediction accuracy, we design a multi-scale loss function to optimize the model’s performance across multiple temporal scales. Experimental results demonstrate that our model achieves significantly lower prediction errors compared to state-of-the-art methods on the NTU RGB+D and Human3.6M datasets, validating its superior performance in skeleton action prediction tasks. This study offers new technical insights for the field of skeleton action prediction and holds great potential for practical applications in intelligent surveillance, human-computer interaction, and healthcare monitoring.