  A convolutional neural network-based classifier is elaborated to retrace the
initial orientation of deformed nucleus-nucleus collisions by integrating
multiple typical experimental observables. The isospin-dependent
Boltzmann-Uehling-Uhlenbeck transport model is employed to generate data for
random orientations of ultra-central uranium-uranium collisions at
$E_\text{beam} = 1\, \text{GeV/nucleon}$. Statistically, the data-driven
polarization scheme is essentially accomplished via the classifier, whose
distinct categories filter out specific orientation-biased collision events.
This will advance the deformed nucleus-based studies on nuclear symmetry
energy, neutron skin, etc.
