To address the adverse effects of missing data in tabular data on subsequent tasks, a method for imputation using diffusion models was proposed. An accelerated diffusion model-based imputation method (PNDM_Tab) was designed aiming at the problem that the original diffusion models being time-consuming during the generation process. The forward process of the diffusion model was realized through Gaussian noise addition, and the pseudo-numerical methods derived from diffusion models were employed to achieve acceleration of the reverse process. Using a network structure combining U-Net with attention mechanisms, significant features were extracted efficiently from the data to predict noise accurately. To provide supervised targets during the training phase, random masking of the training data generated new missing data. Comparative experiments were conducted in nine datasets, and the results showed that PNDM_Tab achieved the lowest root mean square error in six datasets compared to other imputation methods. Experimental results demonstrate that, compared to the original diffusion models, the use of pseudo-numerical methods in the reverse process can reduce the number of sampling steps while maintaining equivalent generative performance.