In order to solve the problems of low accuracy,high computational cost and failure to make full use of space spectrum information of traditional hyperspectral image classification methods,a hyperspectral image classification method based on multi-dimensional parallel convolution neural network (3D-2D-1D PCNN) is proposed in this paper.Firstly,the algorithm uses different dimensions of convolutional neural network (CNN) to extract the spatial spectral features,spatial features and spectral features of hyperspectral image information.Then,the same parallel convolution layer is used to fuse the combined spatial spectral features,spatial features and spectral features.Finally,hyperspectral image information is accurately classified by linear classifier.The proposed method can not only extract the deeper spatial and spectral feature information in hyperspectral images,but also fuse the features of different dimensions of spectral images to reduce the computational cost.Comparative experiments are carried out on Indian Pines,Pavia Center and Pavia University data sets.The results show that the proposed algorithm obtains the optimal results,and the classification accuracy reaches 99.210%,99.755% and 99.770% respectively.