In order to improve the classification performance of hyperspectral images with limited training samples,a hyperspectral image classification Network based on Double Pooling Attention Mechanism (DPAMN) is proposed in this paper.First,the DPAMN uses three-dimensional convolution to extract the spatial and spectral shallow information of hyperspectral images.Second,the double pooling attention mechanism is introduced into DPAMN to enhance the feature extraction ability of the network.Finally,the three-dimensional convolution dense connection module is introduced into the deep layer of the network,which can not only fully extract the spatial and spectral features of hyperspectral images,but also improve the ability of feature discrimination.Experiments show that the overall average accuracy of 95.45％,97.11％,95.30％ and 93.71％ can be achieved on datasets of Indian Pines,University of Pavia,Salinas and Houston 2013,respectively.Compared with the current mainstream advanced methods,the proposed method greatly improves classification performance on four datasets,indicating its strong generalization capacity.