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In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

New-generation intelligent construction places higher demands on digitisation and intelligence of tunnel. Digital twin (DT) effectively supports high-fidelity modelling, virtual-real mapping, and analysis-based decision-making but with research in the initial stage. To begin with, this paper delves into the complexity and uncertainty inherent in tunnel construction, highlighting DT as a promising solution compared to exisiting technologies such as Building Information Modelling. Then, a systematic literature survey is conducted, revealing growing focus on DT research topics. To provide comprehensive insights into DT-related technologies and their application in tunnel construction, this paper clusters literature from perspectives of sensor networks, Internet of Things (IoT), computer vision-based twin data acquisition, communication, natural language processing (NLP), automatic control-based connection, and geometric, semantic, analytical integrated twin modelling. These aspects shed light on potentials and limitations of existing researh in developing a functional DT. In response to the challenges of information richness, timeliness, and analytical capabilities, an improved conceptual framework tailored for tunnel is proposed to close the information and control loop. Finally, the paper discusses the prospects and gaps of DT in theory and practice to leverage DT implementation. • Digital Twin greatly enhances the development of intelligent tunnel construction. • A comprehensive literature analysis is conducted to assess status of Digital Twin technologies in tunnel construction. • A close-loop conceptual framework of Digital Twin is proposed. • Gaps and future prospects of Digital Twin for tunnel construction are discussed. [ABSTRACT FROM AUTHOR]