Abstract: Urban waterlogging is a typical kind of urban natural disasters, which affecting the quality of residents' life.This paper takes a series of waterlogging points produced by urban rainstorm as the research objects, comprehensively considering the influence of urban waterlogging on the work and life of residents, and screens out 21 kinds of data related to the influence degree.At the same time, based on the principle of deep learning, we construct a stacked autoencoder neural network model. With the influence degree labels of urban waterlogging points obtained by analytic hierarchy process method, the relationship between the 21 types of data and the influence degree of waterlogging points is analyzed, which will be applied to the quantitative analysis of the influence of urban waterlogging points.The experimental results show that the proposed model in this paper can describe the relationship between the spatial data and the influence degree accurately. In addition, this model can effectively predict the influence degree of potential waterlogging points, which is not only beneficial to the formulation of the urban waterlogging prevention scheme, but also provides a reference for the design of urban drainage pipe network.