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Students have various learning styles, but they can be trained and analyzed. How to effectively classify students’ styles and better divide them is the main content of the research process and also a means of teaching essence. This is vector product, which can optimize existing learning content and styles through different assignments and vector analysis of students’ personalities. I proposed support vector machine to classify students’ learning styles, and found that its recognition accuracy is over 90%, and the conditions for style and classification are over 20%. Therefore, this is a limited machine that can comprehensively manage students’ learning classification work, promote their level improvement, and support vector machine has an important supportive role in students’ operation process, which can simplify the process of data processing. Enhance the recognition rate of students’ learning styles, provide support for proposing better teaching strategies and optimizing existing teaching content and strategies. Linear sensors can deeply explore and analyze students’ learning methods, judge their rationality and effectiveness, and provide balanced knowledge for teachers and strategy development.

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Combined with the Support Vector Machine Algorithm, the Construction of Student Learning Style Classification and Teaching Strategy Recommendation Model

  • 会议论文

  • 发表日期：

    2026年

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  • Haotian Liu,
  • Jie Wu

摘要

Students have various learning styles, but they can be trained and analyzed. How to effectively classify students’ styles and better divide them is the main content of the research process and also a means of teaching essence. This is vector product, which can optimize existing learning content and styles through different assignments and vector analysis of students’ personalities. I proposed support vector machine to classify students’ learning styles, and found that its recognition accuracy is over 90%, and the conditions for style and classification are over 20%. Therefore, this is a limited machine that can comprehensively manage students’ learning classification work, promote their level improvement, and support vector machine has an important supportive role in students’ operation process, which can simplify the process of data processing. Enhance the recognition rate of students’ learning styles, provide support for proposing better teaching strategies and optimizing existing teaching content and strategies. Linear sensors can deeply explore and analyze students’ learning methods, judge their rationality and effectiveness, and provide balanced knowledge for teachers and strategy development.