In the research of hyperspectral image (HSI) classification based on deep learning, the small sample problem and the lack of classification accuracy caused by not considering global information have not been well solved. In this paper, an HSI classification method based on dense convolution and conditional random field (DCRF) is proposed. First, the 1D-2D convolution kernel is used to extract the spectral-spatial features and the layers are densely connected to obtain a dense convolutional network to reduce parameters. Second, the Max Pooling layer is used as the output layer of the dense convolutional network to improve the accuracy of feature extraction, and the Softmax layer is used to calculate the probability of the category of the sample and preliminary classification. Finally, the conditional random field is used to fully integrate spatial global information to achieve HSI final classification. Extensive experimental results on two HSI data sets have demonstrated the effectiveness of the proposed DCRF when compared with other state-of-the-art methods.