  Giant dipole resonance (GDR) is one of the fundamental collective excitation
modes in nucleus. Continuous efforts have been made to the evaluation of GDR
key parameters in different nuclear data libraries. We introduced multitask
learning (MTL) approach to learn and reproduce the evaluated experimental data
of GDR key parameters, including both GDR energies and widths. Compared to the
theoretical GDR parameters in RIPL-3 library, the accuracies of MTL approach
are almost doubled for 129 nuclei with experimental data. The significant
improvement is largely due to the right classification of unimodal nuclei and
bimodal nuclei by the classification neural network. Based on the good
performance of the neural network approach, an extrapolation to 79 nuclei
around the $\beta$-stability line without experimental data is made, which
provides an important reference to future experiments and data evaluations. The
successful application of MTL approach in this work further proofs the
feasibility of studying multi-output physical problems with multitask neural
network in nuclear physics domain.
