Multi-label learning is a common problem in real application scenarios. The construction of large-scale multi-label datasets often means high cost, so semi-supervised learning technology appears. At present, most semi-supervised learning is mainly used in the field of single label classification. Although semi-supervised learning in the field of multiple labels classification has made some progress, there is still much room for improvement in training time consumption, training effects and the use of potential relationships between labels. A multi-label semi-supervised curriculum learning model was proposed under the dual structure semi supervised course learning under dual structure(SSCD) to solve the above problems. Firstly, a curriculum learning scheme based on dual difference was designed, which greatly reduces the training time and improves the robustness of the model. Secondly, a single attention mechanism was designed to explore the potential relevance between labels. The performance of SSCD in the prediction task was evaluated on three open test datasets, and the results compared with four benchmark models show that the comprehensive indicators of SSCD are optimal in all aspects. Finally, the structure ablation experiment was carried out to prove the effectiveness of the proposed single attention mechanism.