Schematic of the improved galaxy morphology classification system. Panel (a) shows the flow of the galaxy morphology classification system. Panel (b) illustrates feature extraction using ConvNeXt and ViT models to encode the data and extract key features. Encoding results from the same data instances are used to construct positive sample pairs, while the remaining encoding from the ViT model form negative sample sets. The contrastive loss (CL) function is applied to minimize the loss and extract important features. Finally, dimensionality reduction is performed using PCA on the encoded results after contrastive learning.