CiteSpace-based Visualization and Analysis of Support Vector Machines in the Direction of Artificial Intelligence and Machine Learning

The purpose of this review is to provide an in-depth bibliometric analysis of Support Vector Machine (SVM) research literature in the field of Artificial Intelligence and Machine Learning in the Web of Science (WOS) database using the CiteSpace visualization tool. Since its introduction in the early 1990s, SVM has become one of the most important and widely used algorithms in the field of machine learning, especially in the fields of image recognition, bioinformatics, financial risk assessment and natural language processing. This paper reveals the trend of publication volume, major research institutions, hot topics and potential academic frontiers of SVM in the field of Artificial Intelligence and Machine Learning through CiteSpace analysis. In the future, SVM research will evolve deeply while focusing on improving the explanatory power, trustworthiness, and transparency of the models, as well as safeguarding privacy and data security in their design and deployment. This review provides academics with a comprehensive overview of the research progress of SVM in the field of artificial intelligence and machine learning, provides references and insights for subsequent research, and provides data support and decision-making references for the teaching reform of related courses in colleges and universities.