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

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.