Slope tomography is a method to estimate subsurface velocity macromodels from the slopes and traveltimes of local coherent reflection events. In geologically complex areas, the macromodels obtained from slope tomography tend to yield larger errors. To address this issue, this study proposed a method for error correction of the models using deep learning. Specifically, with macromodels determined using slope tomography-based inversion serving as input and corresponding theoretical models as labels, a neural network was trained, yielding a nonlinear mapping from the slope tomography-derived macromodel to the corresponding theoretical model. To ensure that the trained neural network was applicable to measured seismic data, the training samples were generated from the inversion model and migration profiles of measured seismic data. Tests based on the data synthesized using the theoratical model validated the accuracy and effectiveness of the proposed method. The proposed method was then applied to the 2D measured seismic data from beaches and shallow seas, yielding velocity models with elevated precision and depth migration imaging profiles with high quality.