Abstract: On July 22, 2013, an earthquake of MS6.6 occurred at the junction area of the Minxian and Zhangxian Counties, Gansu Province, China. The earthquake had triggered at least 2330 landslides according to the previous studies. This paper takes seismic intensity Ⅷ zone of the earthquake as the study area. Based on the earthquake induced-landslide inventory interpreted from field investigations and visual interpretation of high-resolution satellite images before and after earthquake, five influence factors of slope, aspect, drainage, lithology and fault are selected. Then the landslide susceptibility of the study area is evaluated under GIS platform by applying fuzzy logic model, information value model and Shannon's entropy integrated information value model separately. Results show: (1)Landslides are prone to occur in the central part of the study area. When closer to drainage, it is more susceptible to landslides. By counting landslides in buffer zones of drainage, it finds that majority landslides occurred in 0～50m zone. The percentage of landslides in 0～100m buffer zone is up to 50% of all. (2)The AUC values of three models are 0.8488(Information value model), 0.8502(Shannon's entropy integrated information value model), 0.7640(Fuzzy logic model). It indicates well performances of information value model and Shannon's entropy integrated information value model, and the modest performance of fuzzy logic model. (3)By comparing the areas of each susceptibility levels and landslides proportions in each susceptibility levels of three models, it finds that each level's area ration in Shannon's entropy integrated information value model tends to normal distribution, and the model also has the highest landslide rations in very high and high susceptibility levels. Shannon's entropy integrated information value model increases each unit's information value which leads to a more obvious result. It demonstrates that Shannon's entropy integrated information value model is more suitable for disaster risk evaluation and emergency risk management.