Based on the data of 272 historical landslides from the national landslide catalog and 10 influencing factors, 3 representative quantitative methods, namely, information value model, logistic regression model, and artificial neural network based on frequency ratio model, were used in this paper to evaluate the landslide hazard risk in areas of Tangchang County, Zhouqu County and Wudu District in southern Gansu Province surrounding the Bailong River Basin. The results from these three models showed that the extremely high- and high-risk areas of landslide disaster are mainly distributed along the Bailong River valley. From the hazard zoning map, the result obtained by the artificial neural network model is found to be relatively reasonable, showing not only the trend of centralized distribution along the valley area but also the feature relatively independent to the landslide historical data, which is consistent with previous research results. The accuracy of the three models was tested according to the receiver operating characteristic curve, and the AUC values obtained were 0.818, 0.829, and 0.837, respectively, indicating that all three evaluation results have high reliability. Compared with the other two models, the artificial neural network model based on frequency ratio has better evaluation accuracy and can better predict and evaluate the landslide risk. Elevation, rainfall, lithology, and distance from the road are factors that have greater influence on the evaluation results than other factors, and the importance value of these four influencing factors accounts for 52.1%. The results of this study provide a reference for urban expansion and disaster prevention in the study area.