 biological sequence classification is a rapidly growing field of research driven by the exponential growth of biological sequence data. Machine learning techniques have been used to develop predictive models for mining biological sequence information such as protein structure prediction, gene expression analysis, and protein-protein interaction prediction. This review focuses on the function and modification classification of biological sequences using machine learning. It introduces the steps involved in building an effective model framework for biological sequence data including single cell sequencing data analysis methods and their applications in biology. Finally, it discusses the current challenges and future perspectives of biological sequence classification research. This article was authored by Chanyan AO, Shihu Zhao, Yinsu Wang, and others.