  A deep neural network (DNN) has been developed to generate the distributions
of nuclear charge density, utilizing the training data from the relativistic
density functional theory and incorporating available experimental charge radii
of 1014 nuclei into the loss function. The DNN achieved a root-mean-square
(rms) deviation of 0.0193 fm for charge radii on its validation set.
Furthermore, the DNN can improve the description in both the tail and central
regions of the charge density, enhancing agreement with experimental findings.
The model's predictive capability has been further validated by its agreement
with recent experimental data on charge radii. Finally, this refined model is
employed to predict the charge density distributions in a wider range of
nuclide chart, and the parameterized charge densities, charge radii, and
higher-order moments of charge density distributions are given, providing a
robust reference for future experimental investigations.
