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Zero-shot node classification aims to predict nodes belonging to novel classes that have not been seen in the training. Existing studies focus on transferring knowledge from seen classes to unseen classes, which have achieved good performance in most cases. However, they do not fully leverage the relationships between nodes and overlook the issue of domain bias, affecting overall performance. In this paper, we propose a novel dependency capture and discriminative feature learning (DCDFL) model for zero-shot node classification.