The automated and efficient classification of astronomical spectra is an important research issue in the era of large sky surveys. Most current studies on automatic spectral classification primarily focus on specific data sets and demonstrate outstanding performance. However, the diversity in spectra poses formidable challenges for these classification models, as they exhibit limited capability to generalize across more comprehensive data sets. In response to these challenges, we pioneer a method called the multiscale partial convolution net (MSPC-Net), which amalgamates partial, large kernel, and grouped convolution to facilitate multilabel spectral classification. By harnessing the capabilities of partial convolution, MSPC-Net can effectively reduce the number of model parameters, accelerate the training process, and mitigate the overfitting issue. Integrating large kernel and grouped convolution empowers the model to capture local and global features simultaneously, enhancing its overall classification efficacy. To rigorously evaluate the model’s performance, we generate ten different data sets sourced from the Sloan Digital Sky Survey and Large Sky Area Multi-Object Spectroscopic Telescope. These data sets encompass stellar class, stellar subclass, and full classification, providing a comprehensive assessment across various application scenarios. The experimental results reveal that MSPC-Net consistently outperforms the other models across different data sets, especially demonstrating superior performance in the last two data sets with full classification. Consequently, MSPC-Net is poised to find extensive applications in the detailed classification for large-scale sky survey projects. This work not only addresses the challenges of generalization in spectral classification but also contributes significantly to the advancement of robust models for astronomical research.

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