Abstract: The BP neural network is widely employed in regional landslide susceptibility prediction due to its excellent nonlinear fitting ability and generalization capability. This paper establishes a landslide susceptibility assessment model based on the BP neural network and applies it to Jiuzhaigou, Sichuan Province. The study focuses on 4834 historical landslides caused by the M_S7.0 earthquake in Jiuzhaigou in August 2017. Seventy percent of them are randomly divided into a training sample for landslide susceptibility prediction in Jiuzhaigou, while the remaining 30% form a validation sample set to evaluate the accuracy of the predicted results. Nine influencing factors, including elevation, slope, aspect, distance to parallel seismogenic fault, distance to vertical seismogenic fault, distance to the epicenter, distance to the road, peak ground acceleration(PGA), and lithology, were selected to discuss the control effect of seismogenic faults on earthquake-triggered landslides and conduct the correlation analysis of these influencing factors. The study then summarizes the spatial distribution characteristics of earthquake-triggered landslides. Results indicate that the seismogenic fault, lithology, and slope have a significant influence on the distribution of earthquake-triggered landslides in Jiuzhaigou. The study obtains the prediction map of earthquake-triggered landslide susceptibility in Jiuzhaigou through the model. The results reveal that 73.3% of the landslides are located in the extremely high and high susceptibility areas, which is consistent with the actual distribution of earthquake-triggered landslides. Using the 30% validation sample set to predict the success rate curve, the results show that the model's prediction success rate(AUC) is 0.90, proving that the BP neural network exhibits good accuracy and fitting ability in predicting regional landslide susceptibility. The evaluation results provide a reference for future earthquake-triggered landslide disaster prediction and earthquake prevention and mitigation efforts.