%0 Journal Article %T 基于CiteSpace的计算机视觉领域研究热点与前沿分析
Research Hotspots and Frontiers Analysis in the Field of Computer Vision Based on CiteSpace %A 李林汉 %A 吴子昂 %J Computer Science and Application %P 209-224 %@ 2161-881X %D 2025 %I Hans Publishing %R 10.12677/csa.2025.154093 %X 计算机视觉作为人工智能的核心领域之一，近年来在学术探索和实际应用均取得显著进展并获得广泛关注。本研究基于1992年至2024年间中文文献，采用共现分析、聚类分析以及突现词分析等文献计量方法，通过CiteSpace 6.4.R1软件对CNKI数据库中相关文献进行系统统计与可视化分析。研究从发表时间、期刊分布、高产作者和机构等维度探讨我国计算机视觉领域的演进、前沿及热点。结果表明：我国计算机视觉领域研究以1999年和2014年为节点，期间经历三个阶段性变化，每个阶段研究热点逐步演化、细化，并伴随从技术应用向算法理论的显著范式转变。研究机构和作者呈现出“2 + n”的合作格局，机构方面，以中国农业大学工学院和中国科学院大学为核心形成两大科研机构合作网络；作者方面，以湖南大学的王耀南教授与浙江大学的应义斌教授为核心形成两大科研作者合作网络。此外，通过突现词分析发现，未来计算机视觉研究的主要发展方向聚焦于“深度学习”和“目标检测”两大领域。可见，我国计算机视觉领域研究正处于稳定增长的态势，且“深度学习”和“目标检测”领域的突破有望推动该领域的理论创新与应用发展。
Computer vision, as one of the core fields of artificial intelligence, has made significant progress in both academic exploration and practical applications in recent years, garnering widespread attention. This study is based on Chinese literature published between 1992 and 2024. It employs bibliometric methods such as co-occurrence analysis, cluster analysis, and burst term analysis, using CiteSpace 6.4.R1 software to conduct systematic statistical and visual analyses of relevant literature from the CNKI database. The research explores the evolution, frontier, and hotspots of computer vision in China from multiple dimensions, including publication timeline, journal distribution, high-output authors, and institutions. The results indicate that research in China’s computer vision field has experienced three distinct phases, with 1999 and 2014 as key turning points. Each phase saw the gradual evolution and refinement of research hotspots, accompanied by a significant paradigm shift from technological applications to algorithmic theory. In terms of research institutions and authors, a “2 + n” collaborative network has emerged. Institutionally, two major research networks have formed around China Agricultural University’s School of Engineering and the University of Chinese Academy of Sciences. Authorship-wise, Professor Wang Yaonan from Hunan University and Professor Ying Yibin from Zhejiang University serve as the central figures in two primary collaborative networks. Furthermore, burst term analysis reveals that the future direction of computer vision research will focus on two key areas: “deep learning” and “object detection.” This suggests that China’s computer vision field is experiencing stable growth, with breakthroughs in “deep learning” and “object detection” expected to drive theoretical innovation and practical advancements in the %K 计算机视觉， %K CiteSpace， %K 知识图谱， %K 文献计量法， %K 可视化分析
Computer Vision %K CiteSpace %K Knowledge Graph %K Bibliometrics %K Visual Analysis %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=112041