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<p>Classical Chinese poetry provides a critical basis for revealing historical landscape characteristics and for understanding aesthetic concepts in premodern China. Effectively extracting landscape imagery information from poetry is therefore a prerequisite and key step for guiding the spatial construction of poetic ambience. This study focuses on <i>Zhuzhici</i> (bamboo-branch poems), which is related to the Jiangnan region, and introduces deep learning techniques to develop an automated landscape imagery recognition model tailored to the cultural specificity and linguistic nonstandardness of classical Chinese poetry. The results indicate that the proposed DA-SikuRoBERTa-BiLSTM-CRF model achieves a precision of 87.87%, a recall of 83.05%, and an F1 score of 85.39%, significantly improving the recognition accuracy of landscape imagery elements, including those expressed in single-character forms, functioning as geographically specific proper names, and embedded with implicit emotional connotations or behavioural activities. Furthermore, this study explores potential application pathways of the model for the digital inventory and in-depth mining of historical landscape resources, the refined construction of a place-based landscape imagery knowledge base, and the rapid collaborative reconstruction of cross-modal scene information. These efforts broaden the avenues through which landscape-imagery theories can be translated into spatial planning practices, thereby supporting the creation of poetic dwelling environments.</p>

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Innovative pathways for contemporary expression of landscape imagery in classical Chinese poetry: a deep learning-based automatic recognition model for Zhuzhici

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  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Siqi Gao,
  • Xiwei Xu,
  • Puaypeng Ho,
  • Zijian Zhang,
  • Ruier Chen

摘要

Classical Chinese poetry provides a critical basis for revealing historical landscape characteristics and for understanding aesthetic concepts in premodern China. Effectively extracting landscape imagery information from poetry is therefore a prerequisite and key step for guiding the spatial construction of poetic ambience. This study focuses on Zhuzhici (bamboo-branch poems), which is related to the Jiangnan region, and introduces deep learning techniques to develop an automated landscape imagery recognition model tailored to the cultural specificity and linguistic nonstandardness of classical Chinese poetry. The results indicate that the proposed DA-SikuRoBERTa-BiLSTM-CRF model achieves a precision of 87.87%, a recall of 83.05%, and an F1 score of 85.39%, significantly improving the recognition accuracy of landscape imagery elements, including those expressed in single-character forms, functioning as geographically specific proper names, and embedded with implicit emotional connotations or behavioural activities. Furthermore, this study explores potential application pathways of the model for the digital inventory and in-depth mining of historical landscape resources, the refined construction of a place-based landscape imagery knowledge base, and the rapid collaborative reconstruction of cross-modal scene information. These efforts broaden the avenues through which landscape-imagery theories can be translated into spatial planning practices, thereby supporting the creation of poetic dwelling environments.