  Through ensemble learning with multitasking and complex connection neural
networks, we aggregated nuclear properties, including ground state charge
radii, binding energies, and single-particle state information obtained from
the Kohn-Sham auxiliary single-particle systems. Compared to traditional
density functional theory, our model can more accurately characterize nuclear
ground state information. Aiming at binding energy, the root mean square error
is reduced to 450 keV. Although the complexity involving the nuclear
interaction is skipped, the model has not completely devolved into a black box.
Leveraging the correlation between densities and binding energies, we calculate
the neutron skin thickness of $^{208}$Pb to be 0.223 fm. This model will
advance our understanding of nuclear properties and accelerate the integration
of machine learning into modern nuclear physics.
