Defect identification method for catenary dropper line based on compositional zero-shot learning was proposed, aiming at the problem of insufficient learning of model features and difficulty in effectively improving the recognition accuracy caused by the serious lack of image of catenary defects on site. The visual feature extraction module using ResNet-50 as the backbone network was used to extract image visual features. The pre-trained Word2Vec word vector was used to initialize the node features in the label combination graph. The dependence relationship between the nodes in the label combination graph was learned through the 2-layer graph convolutional networks, thereby optimizing the semantic features of the combined label nodes and improving the final recognition effect. The extracted visual features were matched with the semantic features of the optimized combined label nodes, and the similarity function was constructed to calculate the similarity score between the visual features of the image and the semantic features of the combined label. The prediction of the combined label was completed through the cross-entropy loss. The simulation results show that the proposed method has an average class detection accuracy of 93.5% for seen samples and 86.5% for unseen samples.