With the rapid development of astronomical detection technology, there will be a huge torrent of incoming galaxy images in the coming years, making the automatic galaxy morphology classification a challenging task. To solve the problem of feature selection, the low speed and low accuracy of traditional galaxy morphology classification models, a galaxy morphology classification model based on Inception-v3 neural network with SE (Squeeze and Excitation Network) channel attention mechanism is introduced. We select galaxy images from Sloan Digital Sky Survey (SDSS) for the SE-Inception-v3 model. The test results show that the accuracy of SE-Inception-v3 model is as high as 99.37%, and the F1 scores of spiral galaxy, completely round smooth galaxy, in-between smooth galaxy, cigar-shaped smooth galaxy and edge-on galaxy are 99.33%, 99.58%, 99.33%, 99.41% and 99.16%, respectively. Compared with the MobileNet (Mobile Neural Network) and ResNet (Residual Neural Network) models, the width and depth advantages of SE-Inception-v3 make the classification model have stronger feature extraction capabilities, which provides a new galaxy morphology classification approach for future large-scale sky survey programs.