Landslides have characteristics such as regionality

multipleness

and seriousness. The traditional area landslide spatial prediction method

under massive data condition

has poor real-time performance and strong subjectivity

and the evaluation performance is poor under multiple factors. A distributed regional landslide prediction method based on BP neural network is proposed in this paper. The algorithm is designed as a parallel computing environment of big data processing platform Spark

and the cost function of BP network is designed as two items of mean square error and L2 regularization

which improves generalization ability. Through statistics of the quantitative indicators of landslide factors and the definition of hazard index of monitoring profile

the influencing factors are selected. This approach is applied to massive data mining of 9 landslides in 11 years in Zhongxian area of Three Gorges Reservoir area

which achieves the combination of qualitative analysis and quantitative analysis. All the landslide monitoring sections in the study area were monthly evaluated to determine the risk level

and the spatial prediction of the monthly landslide risk degree was achieved. Experiments show that the fitting accuracy and efficiency obtained by the method are better than gradient-based decision trees and random forest algorithms

and the prediction area landslide risk assessment accuracy is good. This method can be used as a new approach for regional landslide spatial prediction.