  Quasars can be used to measure baryon acoustic oscillations at high redshift,
which are considered as direct tracers of the most distant large-scale
structures in the Universe. It is fundamental to select quasars from
observations before implementing the above research. This work focuses on
creating a catalog of quasar candidates based on photometric data to provide
primary priors for further object classification with spectroscopic data in the
future, such as The Dark Energy Spectroscopic Instrument (DESI) Survey. We
adopt a machine learning algorithm (Random Forest, RF) for quasar
identification. The training set includes $651,073$ positives and $1,227,172$
negatives, in which the photometric information are from DESI Legacy Imaging
Surveys (DESI-LIS) \& Wide-field Infrared Survey Explore (WISE), and the labels
are from a database of spectroscopically confirmed quasars based on Sloan
Digital Sky Survey (SDSS) and the Set of Identifications \& Measurements and
Bibliography for Astronomical Data (SIMBAD). The trained RF model is applied to
point-like sources in DESI-LIS Data Release 9. To quantify the classifier's
performance, we also inject a testing set into the to-be-applied data.
Eventually, we obtained $1,953,932$ Grade-A quasar candidates and $22,486, 884$
Grade-B quasar candidates out of $425,540,269$ sources ($\sim 5.7\%$). The
catalog covers $\sim 99\%$ of quasars in the to-be-applied data by evaluating
the completeness of the classification on the testing set. The statistical
properties of the candidates agree with that given by the method of color-cut
selection. Our catalog can intensely decrease the workload for confirming
quasars with the upcoming DESI data by eliminating enormous non-quasars but
remaining high completeness. All data in this paper is publicly available
online.
