  The classification of galaxy morphology is a hot issue in astronomical
research. Although significant progress has been made in the last decade in
classifying galaxy morphology using deep learning technology, there are still
some deficiencies in spatial feature representation and classification
accuracy. In this study, we present a multi-scale convolutional capsule network
(MSCCN) model for the classification of galaxy morphology. First, this model
improves the convolutional layers through using a multi-branch structure to
extract multi-scale hidden features of galaxy images. In order to further
explore the hidden information in the features, the multi-scale features are
encapsulated and fed into the capsule layer. Second, we use a sigmoid function
to replace the softmax function in dynamic routing, which can enhance the
robustness of MSCCN. Finally, the classification model achieving 97% accuracy,
96% precision, 98% recall, and 97% F1-score under macroscopic averaging. In
addition, a more comprehensive model evaluation were accomplished in this
study. We visualized the morphological features for the part of sample set,
which using the t-distributed stochastic neighbor embedding (t-SNE) algorithm.
The results shows that the model has the better generalization ability and
robustness, it can be effectively used in the galaxy morphological
classification.
