多标签学习是现实应用场景中的一个常见问题。大规模多标签数据集的构建往往意味着高昂的成本，因此出现了半监督学习技术。目前，大多数半监督学习主要用于单标签分类领域，尽管半监督学习在多标签分类领域取得了一些进展，但在训练时间消耗、训练效果和标签之间潜在关系的利用方面仍有很大的改进空间。针对上述问题，提出了一种二元结构下的多标签半监督课程学习模式（semi-supervised course learning under dual structure, SSCD）。首先，设计了一种基于对偶差分的课程学习方案，大大减少了训练时间，提高了模型的稳健性；其次，设计了一个单一注意力机制来探索标签之间的潜在相关性。在3个开放测试数据集上评估了SSCD在预测任务中的性能，并与4个基准模型进行了比较，结果表明SSCD的综合指标在各个方面都是最优的；最后，通过结构消融实验验证了所提出的单注意力机制的有效性。

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.