人工智能领域深度学习研究热点和发展趋势——基于CiteSpace的可视化分析

Research Hot Spots and Development Trends of Deep Learning in Artificial Intelligence—Visualization and Analysis Based on CiteSpace

摘要: 本文基于CiteSpace可视化分析软件，对深度学习赋能人工智能领域的研究热点和发展趋势进行了深入探究。通过文献计量学方法，系统梳理了近五年来深度学习在人工智能领域的应用与研究成果，构建了相关研究的知识图谱。研究揭示了深度学习在人工智能领域的核心研究主题、关键研究节点以及研究热点的演进路径。研究发现，深度学习在图像识别、自然语言处理、语音识别等人工智能子领域的应用取得了显著进展，同时，与大数据、云计算等技术的融合创新也成为研究的新热点。在发展趋势方面，深度学习算法的优化与创新、模型的可解释性与鲁棒性提升、以及深度学习在跨领域融合应用中的挑战与机遇成为未来研究的重点方向。本研究通过可视化分析，为深度学习赋能人工智能领域的研究者提供了直观的研究脉络和热点演进图，有助于深入理解该领域的研究现状和未来发展趋势，为相关研究的深入开展提供有力支持。

Abstract: Based on CiteSpace visualization and analysis software, this paper provides an in-depth exploration of the research hotspots and development trends in the field of deep learning-enabled artificial intelligence. Through bibliometric methods, the application and research results of deep learning in the field of artificial intelligence in the past five years are systematically sorted out, and the knowledge map of related research is constructed. The study reveals the core research themes, key research nodes and the evolution path of research hotspots of deep learning in artificial intelligence. It is found that the application of deep learning in artificial intelligence subfields such as image recognition, natural language processing, and speech recognition has made significant progress, while the integration and innovation with big data, cloud computing and other technologies have become new hotspots for research. In terms of development trends, the optimization and innovation of deep learning algorithms, the improvement of model interpretability and robustness, and the challenges and opportunities of deep learning in cross-domain convergence applications have become the key directions for future research. This study provides researchers in the field of deep learning-enabled artificial intelligence with an intuitive research lineage and hotspot evolution map through visual analysis, which helps to deeply understand the current research status and future development trend of the field, and provides strong support for the in-depth development of related research.