When the bidirectional reflectance distribution function (BRDF) empirical model and semi-empirical model describe the scattering characteristics of the material, the limitation of these models for the description of different scattering characteristics results in large errors between the fitting result and the measured data. To solve the problem, a BRDF model suitable for commonly used materials on space targets with different characteristics is constructed based on deep neural network (DNN). The DNN model, which contains four hidden layers, is based on TensorFlow implementation. It is optimized by AdaDelta gradient descent method, and combined with Dropout method for regularity. Part of the material measurement data is randomly selected as the training sample, and finally the mapping relationships between the BRDF and the angles of the incident zenith, the reflection zenith and the observation azimuth are obtained. A large number of experimental results show that the DNN model has good ability to describe the scattering characteristics of materials, and the fitting error of the DNN model is less than that of the empirical model for the same material.