Before the space debris are effectively cleaned,space debris must be identified.Based on scattering spectrum,four kinds of space debris materials were classified and recognized by convolutional neural network,and the obtained results were compared with that of BP neural network.As material spectral data have the characteristics of low signal-to-noise ratio and poor feature information,spectral signals were pretreated by de-noising,BRDF calculation and normalization.It takes 200 frames as training data and another 50 frames as predicted data for four kinds of space debris materials.The classification and identification tests were done by using two methods.The results show that the overall accuracy of convolutional neural network is 2% lower than one of BP neural network,but its time-consuming is 101 s less than that of BP neural network.When the sampling amount of training data reaches 500 frames in each material,the overall accuracy of convolution nerve network is only 0.05% lower than that of BP neural network,but its time-consuming is 891 s less than that of BP neural network.Convolutional neural network has shown its extraordinary time superiority.The convolutional neural network has a large practical value for classification and identification of space debris materials.