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<p>This study addresses key issues in traditional entrepreneurship education in colleges and universities, including low student participation, insufficient personalization, and the disconnect between theory and practice. The study introduces an innovative personalized entrepreneurship education model based on deep learning techniques, designed to enhance the learning experience. The model consists of three core components: the Personalized Entrepreneurship Characteristic Analysis Network (PECAN), the Personalized Entrepreneurship Education Content Recommendation Model (PECARM), and the Personalized Entrepreneurship Education Effect Evaluation Model (PECEEM). These components work together to analyze individual student data, recommend tailored educational content, and evaluate educational outcomes. Experiments conducted with 5,000 multi-dimensional student data over three years demonstrate the effectiveness of the model. The results show a 15-point increase in entrepreneurial knowledge scores, a 25% improvement in the success rate of practical projects, and a high course satisfaction score of 4.2. This study represents a significant advancement in personalized entrepreneurship education by leveraging deep learning to foster more engaging, relevant, and effective learning environments, ultimately improving both the quality and practical applicability of entrepreneurship education in universities. The findings offer a scalable and operational framework for integrating AI-driven personalized learning into higher education, with broad implications for enhancing student engagement and entrepreneurial capability.</p>

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Application of deep learning in the innovation of personalized entrepreneurship education model in colleges and universities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月11日

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  • Xianxian Yang,
  • Jiangtao Li

摘要

This study addresses key issues in traditional entrepreneurship education in colleges and universities, including low student participation, insufficient personalization, and the disconnect between theory and practice. The study introduces an innovative personalized entrepreneurship education model based on deep learning techniques, designed to enhance the learning experience. The model consists of three core components: the Personalized Entrepreneurship Characteristic Analysis Network (PECAN), the Personalized Entrepreneurship Education Content Recommendation Model (PECARM), and the Personalized Entrepreneurship Education Effect Evaluation Model (PECEEM). These components work together to analyze individual student data, recommend tailored educational content, and evaluate educational outcomes. Experiments conducted with 5,000 multi-dimensional student data over three years demonstrate the effectiveness of the model. The results show a 15-point increase in entrepreneurial knowledge scores, a 25% improvement in the success rate of practical projects, and a high course satisfaction score of 4.2. This study represents a significant advancement in personalized entrepreneurship education by leveraging deep learning to foster more engaging, relevant, and effective learning environments, ultimately improving both the quality and practical applicability of entrepreneurship education in universities. The findings offer a scalable and operational framework for integrating AI-driven personalized learning into higher education, with broad implications for enhancing student engagement and entrepreneurial capability.