  The back-shifted Fermi gas model is widely employed for calculating nuclear
level density (NLD) as it can effectively reproduce experimental data by
adjusting parameters. However, selecting parameters for nuclei lacking
experimental data poses a challenge. In this study, the feedforward neural
network (FNN) was utilized to learn the level density parameters at neutron
separation energy $a(S_{n})$ and the energy shift $\varDelta$ for 289 nuclei.
Simultaneously, parameters for nearly 3000 nuclei are provided through the FNN.
Using these parameters, calculations were performed for neutron resonance
spacing in $s$ and $p$ waves, cumulative number of levels, and NLD. The FNN
results were also compared with the calculated outcomes of the parameters from
fitting experimental data (local parameters) and those obtained from systematic
studies (global parameters), as well as the experimental data. The results
indicate that parameters from the FNN achieve performance comparable to local
parameters in reproducing experimental data. Moreover, for extrapolated nuclei,
parameters from the FNN still offer a robust description of experimental data.
