  Charge radius is one of the most fundamental properties of a nucleus.
However, a precise description of the evolution of charge radii along an
isotopic chain is highly nontrivial, as reinforced by recent experimental
measurements. In this paper, we propose a novel approach which combines a
three-parameter formula and a Bayesian neural network. We find that the novel
approach can describe the charge radii of all $A\ge40$ and $Z\ge20$ nuclei with
a root-mean-square deviation about 0.015 fm. In particular, the charge radii of
the calcium isotopic chain are reproduced very well, including the parabolic
behavior and strong odd-even staggerings. We further test the approach for the
potassium isotopes and show that it can describe well the experimental data
within uncertainties.
