中国传统服饰图像是中华优秀传统文化重要的组成部分，图像内涵丰富，时空跨度大，理解存在歧义，急需一套对语义解读方法，而大量图像标注算法主要关注在各自的垂直领域，传统服饰图像仍然面临着着标注精度亟需提高的挑战。本文以中国传统服饰图像作为研究对象，以字典学习多标签标注方法作为研究方法，提出融合深度多层结构框架的多标签字典学习算法，通过结合字典学习与多层结构框架来提高标注性能。最后通过对比实验验证了该思路的正确。

Chinese traditional clothing image is an important part of Chinese excellent traditional culture. The image connotation is rich, the space-time span is large, and the understanding is ambiguous. There is an urgent need for a set of semantic interpretation methods for it. A large number of image annotation algorithms mainly focus on their respective vertical fields, and traditional clothing images are still facing the challenge of improving the annotation accuracy. This paper takes Chinese traditional clothing images as the research object, takes dictionary learning multi label tagging method as the research method, and proposes a multi label dictionary learning algorithm integrating deep multi-layer structure framework, which improves the tagging performance by combining dictionary learning and multi-layer structure framework. Finally, the correctness of this idea is verified by comparative experiments.