  The muon collider provides a unique opportunity to study the vector boson
scattering processes and dimension-8 operators contributing to anomalous
quartic gauge couplings~(aQGCs). Because of the cleaner final state, it is
easier to decode subprocess and certain operator couplings at a muon collider.
We attempt to identify the anomalous $WWWW$ coupling in the exclusive $WW\to
WW$ scattering in this paper. Since one aQGC can be induced by multiple
dimension-8 operators, the study of one coupling can help to confine different
operators. Meanwhile, singling out the $WW\to WW$ process can help to study the
unitarity bounds. The vector boson scattering process corresponding to the
anomalous $WWWW$ coupling is $\mu^+\mu^-\to
\nu\nu\bar{\nu}\bar{\nu}\ell^+\ell^-$, with four (anti-)neutrinos in the final
state, which brings troubles in phenomenological studies. In this paper, the
machine learning method is used to tackle this problem. We find that, using the
artificial neural network can extract the $W^+W^-\to W^+W^-$ contribution, and
is helpful to reconstruct the center of mass energy of the subprocess which is
important in the study of the Standard Model effective field theory. The
sensitivities and the expected constraints on the dimension-8 operators at the
muon collider with $\sqrt{s}=30$ TeV are presented. We demonstrate that the
artificial neural networks exhibit great potential in the phenomenological
study of processes with multiple neutrinos in the final state.
