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<p>To enhance universities’ risk identification and response capabilities in complex network environments, this study proposes a university network security management model integrating transfer learning and multi-agent collaborative governance mechanisms. It aims to construct an implementable intelligent governance system. Methodologically, this study develops an attention-enhanced transfer residual network that incorporates a channel attention mechanism to strengthen feature selection. The network achieves deep cross-domain alignment through integration with the maximum mean discrepancy method, significantly enhancing recognition accuracy under conditions of limited target domain data availability. Subsequently, a school-family-community collaborative response mechanism based on multi-agent game theory is constructed. The mechanism drives the generation of personalized intervention strategies through a risk scoring function to address response lags and responsibility ambiguity in traditional governance. Results show that the proposed model achieves an accuracy of 95.7%, a recall of 94.8%, a F1 score of 0.951, an Area Under the Curve of 0.973, and detection delay controlled within 29&#xa0;ms. The integrated model proposed in this study demonstrates strong identification capability.</p>

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Transfer learning and AI technology for family school community collaborative model research in university network security management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

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  • Qiongfang Feng,
  • Yang’an Chen

摘要

To enhance universities’ risk identification and response capabilities in complex network environments, this study proposes a university network security management model integrating transfer learning and multi-agent collaborative governance mechanisms. It aims to construct an implementable intelligent governance system. Methodologically, this study develops an attention-enhanced transfer residual network that incorporates a channel attention mechanism to strengthen feature selection. The network achieves deep cross-domain alignment through integration with the maximum mean discrepancy method, significantly enhancing recognition accuracy under conditions of limited target domain data availability. Subsequently, a school-family-community collaborative response mechanism based on multi-agent game theory is constructed. The mechanism drives the generation of personalized intervention strategies through a risk scoring function to address response lags and responsibility ambiguity in traditional governance. Results show that the proposed model achieves an accuracy of 95.7%, a recall of 94.8%, a F1 score of 0.951, an Area Under the Curve of 0.973, and detection delay controlled within 29 ms. The integrated model proposed in this study demonstrates strong identification capability.