As conventional hyperspectral image classification algorithms can not solve the problem of spectral deviation in different images well， a hyperspectral image classification algorithm based on dense convolution and domain adaptive is proposed. First， dense convolution is used in the source domain to perform deep feature learning， and then apply domain adaptive technology to transfer to the target domain. Convolutional neural networks are commonly used for feature learning in the current domain adaptive hyperspectral image classification framework， but when the depth increases， the classification accuracy may decrease due to the disappearance of the gradient. Therefore， this paper introduces dense convolution for deep feature learning， to improve the accuracy of domain adaptive hyperspectral image classification. The effectiveness of the proposed algorithm is verified on the Indiana hyperspectral dataset and Pavia hyperspectral dataset. The overall classification accuracy is 61.06% and 89.63%. Compared with other domain adaptive hyperspectral image classification methods， the proposed method has better classification accuracy.