Analysis of Research Trends in the Field of Museology: Application of Node2Vec and Referential Network Modelling

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Abstract

This study uses the Node2Vec network embedding technology combined with citation network modelling to systematically analyze the knowledge evolution in the research field of museology. Based on 6,726 relevant documents included in the Scopus database from 1948 to 2023, this research constructs a high-dimensional citation network and applies cluster analysis and regression modelling to explore the theme development trends, core research themes, and their influence in this field. The research finds that museology research mainly focuses on cultural heritage protection, digital technology applications, museum education, and public participation, and has shown a trend of interdisciplinary integration in recent years. In addition, with the help of IPY (Intrinsic Publication Year) analysis, this study reveals the inter-generational evolution of research hotspots and their high synchronization with policy revisions and technological innovations (such as the rise of augmented reality technology). The research shows that the knowledge diffusion model of modern research has shifted from traditional collection management to digital-based knowledge sharing and social practice. Finally, this study suggests that future academic research can combine Temporal Graph Attention Networks (TGAT) to improve the representational ability of early literature and multilingual knowledge flows to comprehensively understand the disciplinary development path of museology.

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