The Knowledge Structure and Development Trend in Artificial Intelligence Based on Latent Feature Topic Model

Abstract

Currently, with the rapid development of science and technology, the field of artificial intelligence presents characteristics such as a wide crossover of disciplines and fast update, and the field of artificial intelligence has become a new focus of international competition. As an interdisciplinary field, the field of artificial intelligence has rich knowledge and strategic management significance. This article conducts an in-depth study on the knowledge structure and evolution trends in the field of AI, and the main work is as follows. First, a new potential feature topic model New-LDA is proposed for the study of topic recognition, which enhances the feature learning ability of the traditional LDA model, and makes up for the deficiency of the traditional LDA model in the ability of recognizing topics in complex environments. Second, the knowledge structure in the field of AI is analyzed from two aspects: topic recognition and coword analysis. The time series model is introduced to establish the topic evolution network, and the high-frequency words in three periods are compared and analyzed to find the evolution regular of knowledge structure in the AI domain. Finally, taking the cross-discipline of AI as an example, the thematic evolution of the field and its cross-discipline is analyzed to determine the future development direction and evolutionary trend of the field of AI.