In order to improve the classification performance of hyperspectral images with limited training samples, a hyperspectral image classification network (DPAMN) based on double pooling attention mechanism is proposed in this paper. Firstly, DPAMN uses three-dimensional convolution to extract the spatial and spectral shallow information of hyperspectral images. Secondly, in order to enhance the feature extraction ability of the network, a double pooling attention mechanism is introduced into DPAMN. 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 Indian pines, University of Pavia, Salinas and Houston 2013 data sets. Compared with the current mainstream existing advanced methods, the method proposed in this paper is greatly improved on the four data sets, which shows that the proposed method has strong generalization ability.