With the continuous development of deep learning,machine vision methods based on deep learning are widely used,and the convolutional neural network (CNN) has remarkable effect on hyperspectral imagery (HSI) classification.The sampling position of the convolutional kernel in traditional convolutional networks is fixed and cannot be changed according to the complex spatial structure in HSI,ignoring the features of the data on spatial distribution.To improve the performance of hyperspectral image classification in practical applications,this paper proposes a deformable convolution-based hyperspectral image classification method,which extends the deformable convolution from 2D to 3D considering the high dimensionality of HSI,so as to better extract the features on 3D space.This paper combines the double-branch dual-attention mechanism network (DBDA) structure and 3D deformable convolution,and experiments are conducted on two datasets,Indian Pines (IP) and Botswana (BS),and the experimental results show that the method of this paper achieves better classification accuracy on overall accu-racy (OA),average accuracy (AA) and KAPPA evaluation criteria,and improves OA by 0.15%— 0.23%,AA by 0.21%,and KAPPA by 0.000 〖ＫＧ－１／６〗3—0.001 4 compared with the suboptimal algorithm.