Random forest (RF) is a non-parametric technology which was firstly proposed by Leo Breiman and Cutler Adele in 2001. It was used to deal with the classification and regression problems by gathering a large number of classification tree, which can improve the prediction accuracy. It was applied in the ecological field in recent years. Predicting the spatial distribution of landslide hazard was an important way to achieve disaster prevention and mitigation. The landslide dataset of Shunchang in Fujian Province was taken as case to identify the relationship between mountain landslide occurrence and landslide factors by using RF model and logistic regression (LR) model respectively with landform, meteorological hydrology, soil and vegetation factors. The applicability of RF on landslide prediction in the southern mountain of China was tested by procedure of parameter selection and analysis of model accuracy. The result showed that the goodness of fit of RF was better than that of LR model. The prediction accuracy of RF on the landslide data was 90.8%, while the prediction accuracy of LR was 81.8%. The generalization of RF in the study area was better than that of LR model. The high risk areas and higher risk areas contained 66.05% of the total landslide, which was predicted by RF, while that of LR was 63.34%. The result of model comparison revealed that the RF model was superior to LR model on the mountain landslide prediction in the study area, thus it can be used in the landslide prediction and the division of landslide danger grade with the sample data. In addition, RF model could be applied to other relevant research.