Bibliometric Analysis of Deep Learning Research Trends

This document is a research article that presents a bibliometric analysis of deep learning literature from 2017 to 2023, utilizing Citespace for visualization and analysis. The study samples 1863 articles from the Web of Science core dataset to explore the evolution of research in deep learning. It outlines the methods of co-occurrence analysis, keyword burst analysis, and co-citation analysis to examine the distribution of literature over time, revealing a significant increase in publications from 2017 to 2022, followed by a decline in 2023. The research highlights key areas of interest, including convolutional neural networks and practical applications in various fields such as computer science and engineering. The findings indicate that China, the USA, and England are leading contributors to deep learning research, establishing cooperative relationships across countries. The article also discusses the shift in focus from theoretical research to practical applications, emphasizing the future potential of deep learning technologies.