  The nuclear charge density distribution plays an important role in nuclear
physics and atomic physics. As one of the most frequently used models to obtain
charge density distribution, the two-parameter fermi (2pF) model has been
widely applied in both nuclear physics and atomic physics. Currently, the
feedforward neural network has been employed to study the available 2pF model
parameters for 86 nuclei, and it is found that by introducing A^{1/3} into the
input parameter of the neural network, the accuracy and precision of the
parameter learning effect are improved. Furthermore, the average result of
multiple predictions is more reliable than the best result of a single
prediction, and there is no significant difference between the average result
of density value and of parameter value for the average charge density
distribution. In addition, 2pF parameters of 284 (near) stable nuclei are also
predicted in this work, which provides a reference for the experiment.
