AI-empowered blockchain cybersecurity enhancement and trusted collaborative model design

To enhance the security and coordination of blockchain networks in heterogeneous node environments, this study constructs an AI-empowered trusted security enhancement model. The system architecture designed integrates "off-chain intelligent analysis—on-chain trusted execution—cross-domain policy coordination," combining dynamic graph neural networks, temporal modeling, and reinforcement learning mechanisms to achieve precise threat detection and dynamic coordination among multiple nodes. By incorporating policy intent constraints and multi-objective optimization, the model improves real-time responsiveness and policy consistency. Trust evaluation employs temporal Bayesian updating and reputation decay mechanisms to effectively control the recovery rate of anomalous nodes. Experiments conducted on the Hyperledger Fabric platform using real transaction datasets demonstrate superior performance over comparative methods in threat detection accuracy, coordination latency, and throughput, showcasing robust scalability and resilience.