  Variable stars play a key role in understanding the Milky Way and the
universe. The era of astronomical big data presents new challenges for quick
identification of interesting and important variable stars. Accurately
estimating the periods is the most important step to distinguish different
types of variable stars. Here, we propose a new method of determining the
variability periods. By combining the statistical parameters of the light
curves, the colors of the variables, the window function and the GLS algorithm,
the aperiodic variables are excluded and the periodic variables are divided
into eclipsing binaries and NEB variables (other types of periodic variable
stars other than eclipsing binaries), the periods of the two main types of
variables are derived. We construct a random forest classifier based on 241,154
periodic variables from the ASAS-SN and OGLE datasets of variables. The random
forest classifier is trained on 17 features, among which 11 are extracted from
the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs.
The variables are classified into 7 superclasses and 17 subclasses. In
comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is
generally above approximately 82% and the period accuracy is 70%-99%. To
further test the reliability of the new method and classifier, we compare our
results with the results of Chen et al. (2020) for ZTF DR2. The classification
accuracy is generally above 70%. The period accuracy of the EW and SR variables
is 50% and 53%, respectively. And the period accuracy of other types of
variables is 65%-98%.
