Abstract: The structure and morphology of galaxies can reflect the physical properties of the galaxy itself, and the classification of its morphology is an important part of subsequent analysis and research. The EfficientNet model uses composite coefficients to unify the depth, width, and input image resolution of the deep network model in a more structured manner. This is a new deep network optimization and extension method. This study applies the model to the classification of galaxy data morphology, and the results show that the average accuracy, precision, recall and F1 score (Harmonic mean of precision and recall) based on the EfficientNet-B5 model are all large than 96.6%, which is a significant improvement compared with the classification results of the ResNet-26 model. The experimental results prove that the deep network optimization extension method of EfficientNet is feasible and effective, and can be applied to the morphological classification of galaxies.