Exploring the relationship between spatial distribution and environmental control characters of gully landforms and building accurate extraction model are of great significance for gully landforms extraction in large scale. Based on the artificial extraction of gully landform samples combing with Landsat8 OLI image data with different periods of and DEM data of a typical watershed on the Chinese loess plateau, the random forest model was established to determine the best period for gully landforms extraction and the best combination of gullying features. Then, combined with the optimal model parameters, results of random forest were compared with support vector machine and artificial neural network model to validate the model generalization ability. Our results showed that: (1) The best image period for gully extraction was in December, and the best combination feature set was Red, Blue, elevation (H), SWIR1, positive and negative terrain (PNT), Coastal, texture (GLCM4) and NIR; (2) The distribution of gully landforms in the testing area extracted by three methods had consistently spatial pattern. Based on qualitatively and quantitatively modelling performance, the random forest model presented the best extracting performance, with the average overall accuracy of 80.48%, which was higher by 4.00 percentage and 8.63 percentage compared with the support vector machine model and the artificial neural network model, respectively; (3) The gully landforms accounted for 56.91% of the total testing area and the distribution of gullies in testing area was gradually concentrated from northwest to southeast. The results show that the random forest model has the best comprehensive performance in the study of high-precision gully landforms identification on the Chinese Loess Plateau, and can be widely extended.