  We have investigated the feasibilities and accuracies of the identifications
of RR Lyrae stars and quasars from the simulated data of the Multi-channel
Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources
light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data
and the observation history simulation from the Mephisto-W Survey Scheduler, we
have simulated the $uvgriz$ multi-band light curves of RR Lyrae stars, quasars
and other variable sources for the first year observation of Mephisto-W Survey.
We have applied the ensemble machine learning algorithm Random Forest
Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build
training and test samples and extract ~ 150 features from the simulated light
curves and train two RFCs respectively for the RR Lyrae star and quasar
classification. We find that, our RFCs are able to select the RR Lyrae stars
and quasars with remarkably high precision and completeness, with $purity$ =
95.4 per cent and $completeness$ = 96.9 per cent for the RR Lyrae RFC and
$purity$ = 91.4 per cent and $completeness$ = 90.2 per cent for the quasar RFC.
We have also derived relative importances of the extracted features utilized to
classify RR Lyrae stars and quasars.
