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) and Wide-field Infrared Survey Explore (WISE), and the labels are from a database of spectroscopically confirmed quasars based on Sloan Digital Sky Survey and the Set of Identifications & Measurements and Bibliography for Astronomical Data. 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 (∼5.7%). The catalog covers ∼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 are publicly available online.

• There are currently no refbacks.