  One of the major challenges we face is how to quickly and accurately create
the three-dimensional (3D) density distributions of interstellar dust in the
Milky Way using extinction and distance measurements of large samples of stars.
In this study, we introduce a novel machine-learning approach that utilizes a
convolution neural network, specifically a V-net, to infer the 3D distribution
of dust density. Experiments are performed within two regions located towards
the Galactic anti-center. The neural network is trained and tested using 10,000
simulations of dust density and line-of-sight extinction maps. Evaluation of
the test sample confirms the successful generation of dust density maps from
extinction maps by our model. Additionally, the performance of the trained
network is evaluated using data from the literature. Our results demonstrate
that our model is capable of capturing detailed dust density variations and can
recover dust density maps while reducing the ``fingers of god" effect. Moving
forward, we plan to apply this model to real observational data to obtain the
fine distribution of dust at large and small scales in the Milky Way.
