Aiming at the problems of hyperspectral image with high dimension, a few training samples, over fitting and too many training parameters, we propose an modified dense connection network (DenseNet) combined with spatial spectrum attention mechanism network (MDSSAN). First, the hyperspectral images are analyzed by principal component analysis, and the spatial neighborhoods of the central pixels are input into the modified network model. Then, three-dimensional DenseNet is improved, and the three-dimensional convolution block in the model is decomposed into the sampling convolution of the spatial dimension and the spectral dimension. Finally, the spatial attention mechanism is introduced in the spatial dimension, and the channel attention mechanism is introduced in the spectral dimension to reduce the training parameters of the model and extract more discriminative space-spectrum joint features. Experimental results show that the overall classification accuracy of the MDSSAN model on the Indian Pines, Pavia University, and KSC data sets are 99.43%, 99.74%, and 98.98%, respectively. Compared with other comparison models, the model has faster convergence speed and better classification performance.