The invention discloses a hyperspectral image classification method based on deep learning, and belongs to the technical field of remote sensing image processing. The method comprises the steps that 1, dimension reduction treatment on a hyperspectral image is achieved by obtaining a data sample, conducting layer-by-layer training on an autoencoder network and further adjusting an initial weight value obtained through pre-training by adopting a BP algorithm; a data cube in each pixel neighbourhood in the hyperspectral image is taken as input of a convolutional neural network, a ground object type corresponding to a pixel serves as expected output of the convolutional neural network, the convolutional neural network is trained, the trained convolutional neural network acts on the whole hyperspectral image, and a final high-precision classification result is obtained. According to the method, the defects that in a traditional hyperspectral image classification problem, details are discarded in the dimension reduction process, space information is lost in the classification process, and the classification precision is low are overcome, the good classification precision is achieved, and the method is suitable for classification of various hyperspectral images.