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Few-Shot Learning has had a significant influence on how people live, work, and learn. Physical education is a requirement for a college diploma. Sports management systems, which focus on data collection, organization, and analysis, as well as timeliness and guidance, are one of the current challenges in the field of physical education at the country’s top colleges and universities. The amount of sex in the room is minimal. Time is money when it comes to making college sports decisions, and this paper uses data from physical fitness tests to illustrate this point. Use Few-Shot Learning technology to extract relevant data from the data, allowing teachers to provide more scientific and effective guidance and suggestions to students. The design and implementation of this paper collect data from physical fitness tests in real-time using mobile edge computing, analyze the data, and display the results using machine learning technology, which mines deep features and displays analysis results, can be used to evaluate students’ physical fitness. The data and information in the physical fitness analysis system are more readable and time-saving, allowing students to better understand their true level of physical fitness. Because of the results of data mining, teachers can provide more specific guidance and recommendations for each student’s physical characteristics.

College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology