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.