<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">erd</journal-id><journal-title-group><journal-title>Education Reform and Development</journal-title></journal-title-group><issn>2652-5364</issn><eissn>2652-5372</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/erd.v7i6.11035</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Analysis of Research Trends in the Field of Museology: Application of Node2Vec and Referential Network Modelling</title><url>https://artdesignp.com/journal/erd/7/6/10.26689/erd.v7i6.11035</url><author>PeiRongkang</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>7</volume><issue>6</issue><history><date date-type="pub"><published-time>2025-07-07</published-time></date></history><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.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Duff W, Carter J, Cherry J, 2013, Archival Education and the Need for Cultural Heritage Professionals. Journal of Archival Organization, 11(3–4): 173–208.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Hider P, Kennan M, 2020, Relationships Between Library and Information Science and Museum Studies. Journal of the Association for Information Science and Technology, 71(4): 405–417.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Kim Y, 2012, Integrating Museum Studies Into LIS Curricula. Journal of Education for Library and Information Science, 53(2): 101–115.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Latham K, Simmons J, 2019, Defining the Museum: Past, Present, and Future. Museum Management and Curatorship, 34(5): 453–467.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Sigfúsdóttir I, 2020, Cross-Disciplinary Approaches to Museum Studies: An Analysis. Museum International, 72(1–2): 68–77.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Waibel G, Erway R, 2009, Think Global, Act Local: Library, Archive, and Museum Collaboration. Library Trends, 57(3): 519–527.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Chen C, 2006, CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. Journal of the American Society for Information Science and Technology, 57(3): 359–377.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Chen C, Song M, 2017, Representing Scientific Knowledge: The Role of Citation Analysis. Scientometrics, 111(2): 1527–1542.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B9" content-type="article"><label>9</label><element-citation publication-type="journal"><p>Grover A, Leskovec J, 2016, Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 855–864.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B10" content-type="article"><label>10</label><element-citation publication-type="journal"><p>Perozzi B, Al-Rfou R, Skiena S, 2014, DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 701–710.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B11" content-type="article"><label>11</label><element-citation publication-type="journal"><p>Xia W, Li T, Li C, 2023, A Review of Scientific Impact Prediction: Tasks, Features and Methods. Scientometrics, 128(1): 543–585.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B12" content-type="article"><label>12</label><element-citation publication-type="journal"><p>Geng Y, Zhang X, Gao J, et al., 2024, Bibliometric Analysis of Sustainable Tourism Using CiteSpace. Technological Forecasting and Social Change, 2024, 202: 123310.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B13" content-type="article"><label>13</label><element-citation publication-type="journal"><p>Wang S, Chen Y, Lv X, et al., 2023, Hot Topics and Frontier Evolution of Science Education Research: A Bibliometric Mapping From 2001 to 2020. Science &amp; Education, 32(3): 845–869.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B14" content-type="article"><label>14</label><element-citation publication-type="journal"><p>Liu S, Pan Y, 2023, Exploring Trends in Intangible Cultural Heritage Design: A Bibliometric and Content Analysis. Sustainability, 15(13): 10049.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B15" content-type="article"><label>15</label><element-citation publication-type="journal"><p>Hou Y, Xu L, Chen L, 2022, Hotspots and Cutting‐Edge Visual Analysis of Digital Museum in China Using Data Mining Technology. Computational Intelligence and Neuroscience, 2022(1): 7702098.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
