Keywords
CiteSpace
Co-citation analysis
Deep learning
Submitted : 2025-12-10
Accepted : 2025-12-25
Published : 2026-01-09
Abstract
Taking the highly cited literature related to artificial intelligence over the ten-year span between 2009 and 2019 from the Web of Science database as the data sample, this paper has analyzed the distribution of literature, hot topics of research, frontier research and evolution of artificial intelligence by using CiteSpace and applying dual-map overlay, co-word and co-citation analysis, among other bibliometric approaches. The research findings indicate full-fledged multidisciplinary features of artificial intelligence, but interdisciplinary integration is still at a nascent stage; most of the existing researches are focused on fundamental technologies, while applications are quite limited, with more applications of the computer vision technologies; deep learning is garnering the most interest, while a cutting-edge research theme could be the combination of quantum physics and machine learning.
References
Bengio Y, Courville A, Vincent P, 2013, Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35: 1798-1828.
Bengio Y, 2009, Learning deep architectures for AI. Hanover: Now Publishers.
Bishop C, 2006, Pattern recognition and machine learning. New York: Springer.
Brunette E, Flemmer R, Flemmer C, 2009, A review of artificial intelligence. 2009 4th International Conference on Autonomous Robots and Agents.
Cao J, Zhou Y, 2018, Progress in research on the influence of artificial intelligence on economy. Economic Perspectives, 103-115.
[ Carleo G, Troyer M, 2017, Solving the quantum many-body problem with artificial neural networks. Science, 355: 602-606.
Carrasquilla J, Melko R, 2017, Machine learning phases of matter. Nature Physics, 13: 431-434.
Chen C, Leydesdorff L, 2014, Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. Journal of the Association for Information Science and Technology, 65: 334-351.
Chen C, 2017, Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science, 2: 1-40.
Chen Y, Chen C, Liu Z, et al., 2015, The methodology function of Cite Space mapping knowledge domains. Studies in Science of Science, 33: 242-253.
Chou K, 2011, Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology, 273: 236-247.
Cockburn I, Henderson R, Stern S, 2018, The Impact of Artificial Intelligence on Innovation.
Fan L, Yu H, Jiang Y, et al., 2019, Identification and analysis of research front in artificial intelligence: from a perspective of the comparative study among highly productive institutions. Information Studies: Theory & Application, 42: 16-21.
Guo S, Deng E, Xu L, et al., 2014, iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics, 30: 1522-1529.
Hastie T, Tibshirani R, Friedman J, 2009, The elements of statistical learning: data mining, inference, and prediction. New York: Springer.
Hinton G, Osindero S, Teh Y, 2006, A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18: 1527-1554.
Hinton G, 2006, Reducing the Dimensionality of Data with Neural Networks. Science, 313: 504-507.
Hinton G, 2012, Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29: 82-97.
Hu Y, Ding M, Cao G, 2013, Visual analysis of studies on international artificial intelligence in the new century. Chinese Journal of Medical Library and Information Science, 22: 8-13.
Jiang Y, Wang Y, Fan L, et al., 2019, Identification and analysis of research front in artificial intelligence: a perspective based on comparative study of major countries (regions). Information Studies: Theory & Application, 42: 8-15.
Krizhevsky A, Sutskever I, Hinton G, 2012, Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012),
LeCun Y, Bengio Y, Hinton G, 2015, Deep learning. Nature, 521: 436-444.
Li J, Chen C, 2016, CiteSpace: Text Mining and Visualization in Scientific Literature. Beijing: Capital University of Economics and Business Press.
Li Y, Su C, Jia J, et al., 2017, Analysis of development status of world artificial intelligence based on scientific measurement. Computer Science, 44: 183-187.
Lv W, Xu L, Liu J, et al., 2018, Review on Chinese artificial intelligence research in past decade: bibliometric of knowledge atlas during 2008-2017. Technology Economics, 37: 73-78+116.
Molenberghs P, Cunnington R, Mattingley J, 2012, Brain regions with mirror properties: A meta-analysis of 125 human fMRI studies. Neuroscience & Biobehavioral Reviews, 36: 341-349.
Schmidhuber J, 2015, Deep learning in neural networks: An overview. Neural Networks, 61: 85-117.
Szegedy C, Liu W, Jia Y, et al., 2015, Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Wang L, 2016, Discovering phase transitions with unsupervised learning. Physical Review B 94.
Wang Y, Cao J, Yu H, et al., 2019, Identification and analysis of research fronts in artificial intelligence: a perspective based on global evolution study of the domain. Information Studies: Theory & Application, 42: 1-7.
Wright J, Yang A, Ganesh A, et al., 2008, Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31: 210-227.
Yu K, Jia L, Chen Y, et al., 2013, Deep learning: yesterday, today and tomorrow. Journal of Computer Research and Development, 50: 1799-1804.
Zhang C, Ding K, Jia F, 2012, Research by scientometrics and information visualization in international artificial intelligence fi field——based on AAAI conference proceeding papers. Library and Information Service, 56: 69-76.
Zou B, Wang Y, Cao J, et al., 2019, Identification and analysis of research front in artificial intelligence: a perspective based on multi-attribute comprehensive study of highly productive authors. Information Studies: Theory & Application, 42: 22-27.