Domain adaption transfers the source domain knowledge to the target domain to improve the classification accuracy of hyperspectral image classification model for features in different scenes. The development of domain adaptation classification methods for hyperspectral images is rapid， however， there is a lack of comparative analysis for domain adaptation methods. Therefore， the domain adaptation classification methods are classified into four categories： Distribution Adaptation， Feature Selection， Subspace Learning， and Deep Domain Adaptation. In this paper， eight typical methods are selected and three standard hyperspectral datasets from Pavia Center， Pavia University and HyRANK are used to design the comparison experiments. The experimental results show that the deep domain adaptation methods are more advantageous， among which the overall classification effect and computational efficiency of the topological structure and semantic information transfer network method are the best overall.