Well integrity evaluation is of great significance to the production life and productivity of oil and gas wells, as well as the protection of reservoir layers. However, traditional sonic logging technology has been unable to meet the current requirement for evaluating unconventional oil and gas resources. For instance, it falls short when it comes to detecting cementing quality between the cement and formation, as well as achieving a quantitative assessment of annulus. In addition, factors such as mud invasion and wellbore damage have the potential to compromise the integrity of the cement sheath and lead to radial alteration in the medium in the wellbore, which can affect cementing quality. To address these issues, a novel approach based on deep learning is proposed to reconstruct slowness models from wellbore data using a fully connected neural network (FCNN) to evaluate the cementing quality. This interpretation workflow requires forward modeling with the three-dimensional dyadic Green’s function to explore borehole acoustic signals and thus generate training and test datasets. Afterward, the spatial feature maps are learned to reconstruct the slowness models by utilizing all the wellbore data. Additionally, the Real-ESRGAN method is applied to borehole imaging to further improve the above inversion. The inversion results regarding the slowness value, wellbore structure, and interface are more consistent with the true model. Finally, the image pixels are optimized using the thresholding method, resulting in an image of superior quality in terms of sharpness and detail compared to previous results. This research reveals that the FCNN-based method can effectively invert the radial profile of slowness around the wellbore from monopole sonic logging data, and the Real-ESRGAN method can further enhance the imaging results to better approximate the true models. This method has also been applied to realistic borehole scenario models, demonstrating its feasibility.