  With the development of a series of Galaxy sky surveys in recent years, the
observations increased rapidly, which makes the research of machine learning
methods for galaxy image recognition a hot topic. Available automatic galaxy
image recognition researches are plagued by the large differences in similarity
between categories, the imbalance of data between different classes, and the
discrepancy between the discrete representation of Galaxy classes and the
essentially gradual changes from one morphological class to the adjacent class
(DDRGC). These limitations have motivated several astronomers and machine
learning experts to design projects with improved galaxy image recognition
capabilities. Therefore, this paper proposes a novel learning method,
``Hierarchical Imbalanced data learning with Weighted sampling and Label
smoothing" (HIWL). The HIWL consists of three key techniques respectively
dealing with the above-mentioned three problems: (1) Designed a hierarchical
galaxy classification model based on an efficient backbone network; (2)
Utilized a weighted sampling scheme to deal with the imbalance problem; (3)
Adopted a label smoothing technique to alleviate the DDRGC problem. We applied
this method to galaxy photometric images from the Galaxy Zoo-The Galaxy
Challenge, exploring the recognition of completely round smooth, in between
smooth, cigar-shaped, edge-on and spiral. The overall classification accuracy
is 96.32\%, and some superiorities of the HIWL are shown based on recall,
precision, and F1-Score in comparing with some related works. In addition, we
also explored the visualization of the galaxy image features and model
attention to understand the foundations of the proposed scheme.
