Hyperspectral image has been considered as one of the greatest research directions in the remote sensing science due to its advantages of high spectral resolution as well as allowing for the synchronous acquisition of both images and spectra of objects.Most conventional hyperspectral image classification methods, however, are based on “shallow” handcrafted features, and highly relies on expert knowledge, which are difficult to meet the current technical requirements.In recent years, with the wide application of convolutional neural networks in the field of artificial intelligence, hyperspectral image classification methods based on convolutional neural networks have achieved breakthroughs in classification accuracy and speed.Firstly, hyperspectral image classification methods are introduced, and the limitations of traditional classification methods are analyzed.Secondly, according to the different extraction methods of hyperspectral image features by convolutional neural networks, the algorithm is divided into three types:Spectral features, spatial features and spatial-spectral features, and the merits and demerits are analyzed.Finally, some suggestions are put forward for the insufficient sample training, practical application and classification results of hyperspectral image classification.