  Precise measurement of antineutrino spectra produced by isotope fission in
reactors is of great significance for studying neutrino oscillations, refining
nuclear databases, and addressing the reactor antineutrino anomaly. This work
reports a method utilizing a feedforward neural network (FNN) model to
decompose the reconstructed measured prompt energy spectrum observed by a
short-baseline reactor neutrino experiment and extract the antineutrino spectra
produced by the fission of major isotopes such as $^{235}$U, $^{238}$U,
$^{239}$Pu, and $^{241}$Pu in a nuclear reactor. We present two training
strategies for this model and compare them with the traditional $\chi^2$
minimization method, analyzing the same set of pseudo-data for a total exposure
of $(2.9\times 5\times 1800)~\rm{GW_{th}\cdot tons\cdot days}$. The results
show that the FNN model not only converges faster and better during the fitting
process but also achieves relative errors in the extracted spectra within 1\%
in the $2-8$ MeV range, outperforming the $\chi^2$ minimization method. The
feasibility and superiority of this method have been validated in this study.
