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

Measuring the anomalous quartic gauge couplings in the $W^+W^-\to W^+W^-$ process at muon collider using artificial neural networks