  In this paper, we introduce a Unet model of deep learning algorithms for
reconstructions of the 3D peculiar velocity field, which simplifies the
reconstruction process with enhanced precision. We test the adaptability of the
Unet model with simulation data under more realistic conditions, including the
redshift space distortion (RSD) effect and halo mass threshold. Our results
show that the Unet model outperforms the analytical method that runs under
ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in
correlation coefficient and 27% in average coherence. The deep learning
algorithm exhibits exceptional capacities to capture velocity features in
non-linear regions and substantially improve reconstruction precision in
boundary regions. We then apply the Unet model trained under SDSS observational
conditions to the SDSS DR7 data for observational 3D peculiar velocity
reconstructions.
