The effective organization and utilization of dis-persed archival resources remain a significant challenge in archival management services. This paper proposes a structured graph-based knowledge modeling framework to transform dispersed archival records into a structured knowledge system for enhanced analysis and knowledge discovery. The framework uncovers latent knowledge embedded within archival records by applying metadata extraction, semantic annotation, ontology construction, and graph-based visualization methods. Specifically, we first design a metadata schema that integrates both standard and customized elements to maximize the machine-readable representation of archival content. Based on this schema, a domain ontology comprising five core classes is constructed to systematically represent the semantic structure of the archival collection. Secondly, we develop an intelligent processing platform to handle multimodal resources, supporting automatic metadata extraction, manual annotation, and comprehensive data prepro-cessing. Lastly, we take unstructured COVID-19-related archival materials as a case study to demonstrate how the system organizes and links archival knowledge, which significantly improves the retrieval, reuse, and discovery of valuable resources.

Digital technology advances have converted traditional archives into digital formats, broadening data diversity and posing new management challenges. This paper introduces a novel framework for Multimodal Archival Data Ecosystems, designed to enhance the management, accessibility, and utilization of archival data across diverse modalities. The need for such a framework arises from critical challenges in archival resource management, including the absence of systematic organizational structures, inefficiencies in inter-regional communication, and insufficient strategies for preserving and enhancing archival material values. Traditional archival systems struggle with these issues, especially in the face of growing data diversity and volume due to advancements in digital technologies. Our method is grounded in information ecology theory, leveraging recent advancements in information science. We propose a multimodal approach that integrates various data types and sources, enhancing the analytical and knowledge services capabilities of archival resources. This framework promotes efficient knowledge exchange by implementing collaborative mechanisms among archival stakeholders and incorporating intelligent archiving services to improve user interaction and resource accessibility. We conducted a comprehensive analysis of the ecosystem’s structure and operational mechanisms, emphasizing the integration of collaborative and intelligent services to meet the dynamic needs of users and keep pace with ongoing technological advancements. This study supports innovative practices that aim to transform traditional archival systems into dynamic, multimodal ecosystems capable of handling the complexities of modern archival data.