In order to investigate the uncertainty of landslide disasters affected areas in mountain cities, the typical landslides in the central urban area of Chongqing were selected as the research objects, and the spatial distribution characteristics of historical landslide disaster points were analyzed by the nearest neighbor index, spatial hotspot detection and kernel density estimation methods. A landslide factor database was established with 12 influencing factors including elevation, slope, aspect, landform type, geological lithology, soil type, soil erosion, rainfall, water system, land use, normalized difference vegetation index (NDVI), and population density. A neural network model was used to analyze driving factors of spatial distribution characteristics of landslide disasters and quantitatively calculate the contribution weight of each influencing factor. The accuracy of the model was evaluated with the receiver operating characteristic (ROC) curve. The results obtained from the nearest neighbor index analysis show that the historical landslide disaster points in the study area are clustered, and the spatial hotspot detection and kernel density estimation indicate that Yuzhong District, Shapingba District, and northern Banan District are the areas where the landslides are most concentrated. Among all the factors, population density, land use and rainfall occupy the highest weight, while the weight of aspect and road are the lowest. The area value under the ROC curve (AUC) is 0.917, indicating that the model can accurately reflect the impact of landslide influencing factors in the area.