  The machine-learning techniques have shown their capability for studying
phase transitions in condensed matter physics. Here, we employ the
machine-learning techniques to study the nuclear liquid-gas phase transition.
We adopt an unsupervised learning and classify the liquid and gas phases of
nuclei directly from the final state raw experimental data of heavy-ion
reactions. Based on a confusion scheme which combines the supervised and
unsupervised learning, we obtain the limiting temperature of the nuclear
liquid-gas phase transition. Its value $9.24\pm0.04~\rm MeV$ is consistent with
that obtained by the traditional caloric curve method. Our study explores the
paradigm of combining the machine-learning techniques with heavy-ion
experimental data, and it is also instructive for studying the phase transition
of other uncontrollable systems, like QCD matter.
