To address the limitations of restricted receptive-field scales and insufficient exploration of additional dimensional information in existing Transformer-based image super-resolution networks， this paper proposed a multi-dimensional aggregation transformer network. First， a multi-scale interaction modulation module was designed to extract multi-scale features from low-resolution images， enhancing the diversity of information flow. Second， a spatial–channel interaction module was integrated into transformer layers， employing four types of attention mechanisms to fully extract key features and achieve effective feature fusion， thereby improving model performance. Third， a feature-reuse transformer module was proposed to explicitly model inter-layer feature relationships， enabling precise extraction and efficient reuse of important features. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art algorithms on five benchmark datasets. Specifically， in super-resolution tasks with various magnification factors， it achieves an average improvement of 0.26 dB in peak signal-to-noise ratio and 0.002 4 in structural similarity index measure compared to Swin Transformer-based methods， producing clearer reconstruction results. These findings validate the effectiveness of the proposed approach and its strong potential for practical applications in image super-resolution tasks.