Abstract: This study developed a rapid method for aerodynamic heating prediction in high-speed lifting bodies. A dataset comprising 540 simulation cases was constructed through parametric modeling, encompassing 15 three-dimensional configurations and 36 flight conditions. To facilitate efficient neural network training, a geometry-aware preprocessing method based on surface slicing and interpolation was proposed, converting complex surface and geometry information into matrix representations. Subsequently, a D-TMU model based on deep learning was introduced. The model employed an encoder–decoder architecture integrating transformer modules, multilayer perceptrons, and UNet structures, with depthwise over-parameterized convolutions replacing standard convolution layers. D-TMU directly predicted surface heat flux distributions without iterative computations from input geometry, pressure distribution, and flight conditions. Validation results demonstrated the predictive performance of the model, with mean prediction errors of 1.21% on the test set, 1.19% in high heat flux regions, and 0.97% at key points. The average inference time per case was 0.03 seconds, representing computational acceleration a speedup of approximately six orders of magnitude compared over traditional CFD methods. These results indicated that D-TMU effectively captured global geometric characteristics and local feature interactions, maintaining achieving high predictive accuracy and computational efficiency. In addition, the model exhibited promising generalization capability for lifting body configurations beyond the training set.