In order to extract the features of spatial information and spectral information in hyperspectral image, a 3-D convolutional recursive neural network (3-D-CRNN) hyperspectral image classification method was proposed. Firstly, 3-D convolutional neural network was used to extract local spatial feature information of target pixel, then bidirectional circular neural network was used to train spectral data fused with local spatial information, and joint features of spatial spectrum were extracted. Finally, Softmax loss function was used to train classifier to realize classification. The 3-D-CRNN model did not require complex pre-processing and post-processing of hyperspectral image, which can realize end-to-end training and fully extract semantic information in spatial and spectral data. Experimental results show that compared with other deep learning-based classification methods, the overall classification accuracy of the method in this paper is 99.94% and 98.81% respectively in Pavia University and Indian Pines data set, effectively improving the classification accuracy and efficiency of hyperspectral image. This method has some enlightening significance for feature extraction of hyperspectral image.