Digital techniques have been used to record cultural heritage with high-quality imagery and documentation. However, some historical properties are completely or partially labeled, and some are externally impaired, which reduces their attraction and causes loss of value. Classification of images is one of the most significant digital-era tasks. As for cultural heritage, classification processes must be developed well and less computer-intensive because image classification typically requires huge data. In this article, Deep Learning assisted Intangible Cultural Heritage Management (DLICHM) has been proposed based on digital tools. A deep learning model automatically analyzes damaged images at the computing terminal based on the gathered data. This approach focuses on the automated annotation and completion of metadata by new deep learning and annotation approaches. It tackles visually damaged objects through a novel approach to image reconstruction focused on supervised and unsupervised learning. The findings suggest that deep learning provides an adequate solution to the semantic annotation of shared cultural heritage data.

数字技术已被用于以高质量图像和文献记录文化遗产。然而，一些历史财产被完全或部分标记，还有一些受到外部损害，这降低了它们的吸引力并导致其价值损失。图像分类是数字时代最重要的任务之一。对于文化遗产，必须开发良好的分类过程，减少计算机密集度，因为图像分类通常需要大量数据。在本文中，深度学习辅助非物质文化遗产管理 (DLICHM)已经提出基于数字工具。深度学习模型根据收集的数据自动分析计算终端的损坏图像。这种方法侧重于通过新的深度学习和注释方法自动注释和完成元数据。它通过一种专注于监督和无监督学习的新型图像重建方法来处理视觉损坏的物体。研究结果表明，深度学习为共享文化遗产数据的语义注释提供了充分的解决方案。