[Objective] The susceptibility of regional debris flow was analyzed and an efficient and rapid analysis model proposed for the debris flow disaster prediction research Taonan area of Jilin Province.[Methods] Owing to the current shortcomings of most probabilistic statistical models that have low prediction rates, the random forest algorithm with obvious effect built on an artificial intelligence algorithm was used to study the northwestern mountainous area of Taonan City, Jilin Province. The elevation, slope, aspect, plane curvature, profile curvature, river, normalized difference vegetation index, topographic humidity index, land use, and lithology were selected. A random forest debris flow susceptibility assessment model for the study area was constructed using these factors. The frequency ratio method was used to model and compare with the random forest model. The model effect was verificated using a receiver operating characteristic curve and an area under the curve.[Results] Random forests were used to analyze the sensitivity of debris flow in the study area and were divided into five sensitivity areas by GIS. The disaster points above the high sensitivity area accounted for 82.3%. The success rate and prediction rate of the verification model were 88.4% and 90.4%, respectively, which was better than the success rate and prediction rate of frequency ratio (86.4% and 75.1%).[Conclusion] The sensitivity analysis of debris flow in the Northern Taonan City was performed using the random forest method and compared with the frequency ratio method. The random forest results were reliable and accurate.