Abstract: Semi-supervised multi-label learning employs labeled and unlabeled data to train a model, which effectively achieves good results and reduces the labeling cost of multi-label data. Therefore, semi-supervised multi-label learning has attracted many researchers dedicated to this field. However, in the semi-supervised annotation process, due to the large number of labels, it is a common situation that some labels lack of samples, and these labels are called open vocabulary. It is difficult for the model to learn the label information of the open vocabulary, which leads to the degradation of its performance. To address the above problem, this paper proposes a semi-supervised open vocabulary multi-label learning method based on graph prompting. Specifically, this method uses a graph neural network via prompt to fine-tune the pre-trained model and explore the relationship between open vocabulary and supervised samples. By using images and text, we construct a graph neural network as the input of text for the pre-trained model. Furthermore, by leveraging the generalization ability of the pre-trained model on open vocabulary, pseudo-labels are generated for unsupervised samples. Then we use pseudo-labels to train the classification layer to enable the model to achieve better performance in classifying open vocabulary. Experimental results on multiple benchmark datasets, including VOC, COCO, CUB, and NUS, consistently demonstrate that the proposed method outperforms existing methods and achieves state-of-the-art performance.