Eclipse attacks， as a significant threat to blockchain network layer， can isolate the attacked node from entire network by controlling its network connections， thus affecting its ability to receive block and transaction information. On this basis， attackers can also launch double-spending and other attacks， which causes substantial damage to blockchain system. To address this issue， a dynamic detection method of eclipse attacks for blockchain node analysis was proposed by incorporating deep learning models. Firstly， Node Comprehensive Resilience Index （NCRI） was utilized to represent multidimensional attribute features of the nodes， and Graph ATtention network （GAT） was introduced to update the node features of network topology dynamically. Secondly， Convolutional Neural Network （CNN） was employed to fuse multidimensional features of the nodes. Finally， a Multi-Layer Perceptron （MLP） was used to predict vulnerability of the entire network. Experimental results indicate that an accuracy of up to 89.80% is achieved by the method under varying intensities of eclipse attacks， and that the method maintains stable performance in continuously changing blockchain networks.