The lightcurves of asteroids are essential for determining their physical characteristics, including shape, spin, size, and surface composition. However, most asteroids are missing some of these basic physical parameters due to lack of photometric data. Although a few telescopes or surveys are specially designed for photometric lightcurve observations of asteroids, many ground-based and space-based sky surveys for hunting new exoplanets, transient events, etc., should capture numerous small Solar system objects. This will benefit the physical studies of these objects. In order to extract data of these moving objects from time-domain photometric surveys, we have developed a new method using the model tree algorithm in the field of machine learning. A dedicated module is built to automatically identify moving objects in data set and extract their photometric and astrometric data. As the first application of this novel method, we have analysed data in five fields of the Yunnan-Hong Kong wide field photometric (YNHK) survey, from which 538 lightcurves of 211 asteroids are successfully extracted. Meanwhile, we also tested the method based on the data from NASA's Transiting Exoplanet Survey Satellite, and the result proves the reliability of our method. With derived lightcurves of 13 asteroids from the YNHK survey, we have determined their synodic spin periods, among which the periods of four asteroids are estimated for the first time. In future, we are going to apply this method to search for small objects in the outer part of the Solar system from the Chinese Space Station Telescope survey. © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.