Existing single-image super-resolution reconstruction methods based on diffusion probabilistic model are deficient in spatial feature information extraction, failing to fully mine the relevant information, as well as redundancy in the computational process. In this paper, a single-image super-resolution reconstruction method incorporating multidimensional attention network is designed. First, multidimensional attention is proposed on the basis of the SRDiff diffusion model, which combines channel attention, self-attention and spatial attention to enhance the model's ability to capture features at different scales, so that more details and better global consistency can be retained at the same time when recovering high-resolution images. Second, PConv partial convolution is introduced to accurately extract the spatial features of the image, improve the quality of the super-resolution results, and significantly reduce the amount of computation, thus improving the operational efficiency of the model. Under the condition of magnification factor of 4, this paper's method is compared with other methods on five test sets, and the results show that the peak signal-to-noise ratio of this paper's method is improved by 0.762 dB compared with the average of the other compared methods, and the structural similarity is improved by 0.082 compared with the average of the other compared methods.The proposed method in this paper possesses subjectively more delicate details and more excellent visual effects, objectively has higher peak SNR values and structural similarity values.