In order to reduce the dependence of the convolutional neural network model on training samples and improve the classification performance of hyperspectral images,this paper propo ses a hyperspectral image classification algorithm combining Gabor filtering and 3D/2D convolution.Firstl y,the three-dimensional Gabor filter is used to process the original hyperspectral data to generate space-spe ctrum tunnel information.Secondly,use the three-dimensional convolution operati on to extract the deep features of the generated space-spectrum tunnel information,and then use the two-dimensional convolution to further extract th e spatial information of the image. Finally,the hyperspectral image classification is completed by the Softmax clas sifier.In order to verify the performance of the model,the method in this paper is compared with CNN,2D-CNN ,3D-CNN-LR and SSRN algorithm on the data sets of Indian Pines,Pavia University and Salinas.Experi mental results show that the overall recognition accuracy of this method reaches 99.51%,99.94% and 99.99%,which are higher than other methods.The method in this paper can effectively improve the classification accuracy,is a s imple and efficient hyperspectral image classification algorithm.