Rock ore slice identification is a task that requires a high level of expertise.Manual identification often results in unavoidable subjective errors and is highly inefficient.Deep learning image recognition technology can efficiently perform rock ore slice identification,but training deep learning models requires a large amount of annotated data.Therefore,it is important to find efficient ways to utilize limited annotated data.By adopting a multi-label classification approach,a classifier can be trained on a labeled dataset,and then this classifier is used to generate pseudo-labels for a large number of unlabeled rock ore slice images.Finally,the model is retrained using the labeled training data and all the unlabeled data.The results show that the use of multi-label classification approach for identification of rock ore slice structures and minerals is feasible.Additionally,this paper employs a semi-supervised learning approach to train the model and improve the model’s generalization ability without requiring a large amount of manual annotation.