Research on Interdisciplinary Characteristics: A Case Study in the Field of Artificial Intelligence

Abstract

In order to show the interdisciplinary laws of the subject fields and analyze the interdisciplinary structure of the field and the contents of knowledge research, a clear-structured framework of interdisciplinary feature recognition is constructed. Taking the field of artificial intelligence as an example, this paper analyzes it from the aspects of disciplinary diversity and disciplinary coherence. Disciplinary diversity is characterized by the distribution of disciplines, Shannon entropy and Rao-Stirling index. Disciplinary coherence is based on network analysis, which mainly includes discipline citation network and core, important and marginal discipline-keyword cooccurrence network at a finer granularity level. These two levels more comprehensively show the distribution and flow of discipline knowledge in this field. Through empirical research, it is found that the field of artificial intelligence involves a variety of disciplines, and its interdisciplinary situation evolves over time, forming clusters of disciplines with tight knowledge flow. In addition, the cross-topic domains of disciplinary research are also different. The results show that the framework system has certain reliability and can provide reference for related research.