This conceptual study explores the development and enhancement of a multilingual translation system for the Qilu Library, a comprehensive cultural repository, under low-resource language conditions. It examines the challenges associated with translating culturally specific and linguistically scarce content, and proposes strategic frameworks for system construction and optimization. By analyzing current machine translation technologies, corpus development methods, and adaptive learning models, this article identifies key areas for improvement in handling low-resource languages, particularly those relevant to Shandong's cultural heritage. The study emphasizes the integration of human-AI collaboration, data augmentation, and transfer learning as vital strategies for improving translation quality and system robustness. Furthermore, it introduces the role of knowledge graphs in cultural concept disambiguation and active learning for efficient resource allocation. The goal is to provide a scalable and culturally sensitive translation model that can be adapted to other regional cultural projects, thereby contributing to the global dissemination of local knowledge and offering practical insights for digital heritage preservation.
Cite this paper
Zhang, W. , Zeng, Y. and Liu, X. (2025). Construction and Optimization Strategies of a Multilingual Translation System for the Qilu Library in Low-Resource Language Scenarios. Open Access Library Journal, 12, e14686. doi: http://dx.doi.org/10.4236/oalib.1114686.
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