Exploring The Evolutionary Pattern On The Academic Research And Technlogy Application Of Artificial Intelligence:A Perspective Of Bibliometrics And Topic Modeling

Since the beginning of the 21 st century,western developed countries represented by the United States and emerging economies represented by China have been vigorously promoting the development of artificial intelligence(AI).There is still a certain gap between the overall scale and comprehensive strength of China's AI industry and the United States,but some fields are already in the lead.In recent years,the rapid growth of AI related research literatures and patents has led to a relatively lagging study of important issues such as scientometric itself,evolutionary pattern and application frontiers in AI researchs and applications,the explanation of major scientific issues is not sufficient,and even some areas still have research blind spots.Quantitative analysis,topic modeling and content mining for artificial intelligence research and application evolution models undoubtedly have important decision-making reference value and management practice significance for the formulation of relevant science and technology or industrial policies in China.In order to further describe the evolutionary pattern of AI related research and application in the past 30 years,this paper has collected more than 220,000 document data and more than 29,000 published patent data,from the comprehensive perspective of bibliometrics and topic modeling,quantitative research is to be conducted and heuristic management and technology policy recommendations are attempted to be proposed.First of all,based on the AI literature data collected by WOS(Web of Science)and the patent data collected by Derwent Innovations Index,this paper uses the bibliometric analysis tool to draw the knowledge graph of AI research and application,and explores the research hotspots of AI and the evolutionary pattern of important technology frontiers in the past 30 years.Also,the research and application hotspots are classiflied and the topic granularity is refined.Secondly,based on the LDA(Latent Dirichlet Allocation)model,this paper combines some new concepts and measurement methods(distance between topics,patent transformation strength and patent transformation efficiency,etc.),and proposes a comprehensive analysis framework.as well as constructs the abovementioned artificial intelligence related literature and patent data.From the perspective of context mining,the model extracts the artificial intelligence sub-topics in different time periods,and further quantifies the theme evolution process and technology transformation mode.The results of the topic modeling experiment also confirm the reliability of the new measurement indexes.As an information system and knowledge management related master's thesis,the main innovations of this paper are as follows:(1)Exploring the connotation and boundary of artificial intelligence from a relatively systematic perspective,and giving a more comprehensive literature and patent search strategy.(2)Proposing the concept and calculation method of distance between topics,and the analysis process of content evolution of research topics is further quantified.A comprehensive analysis framework based on traditional LDA algorithm is also proposed.(3)From the coupling perspective of the literature-patent novelty,two concepts and measurement formulas of patent transformation strength and patent transformation efficiency are proposed,which are used to evaluate the degree of technological transformation of AI,thus helping to identify potential innovation opportunities in the field of artificial intelligence technology applications.