Detection and identification of the evolution of research topics in a discipline has important significance for researchers to grasp its research status and development trend. Visual analysis can show the relationship between themes based on topics recognition,help users to enhance their perception and cognition,and to find useful information quickly in a field on the research status,research hotspots and development trends,and to digest,understand and effectively analyze vast amounts of information. However,discipline topics evolution is a complex process and there are many variables,such as the intensity,structure and content of topics. The single dimension visualization analysis causes the information overload,leading to three problems:perceptive limitations,cognitive limitations,and performance limitations.
The experiment on ‘big data study in library and intelligence field in the past 10 years’ proves the visualization analysis method proposed in this paper could effectively demonstrate the complicated process of topics evolution in a discipline. Compared with the other visualization analysis methods,the proposed method based on topic strength,structure and internal basic knowledge unit evolution could visualize and analyze the core research points,the primary research methods,the key technology topics evolution path and trends and the evolution trend from the macro-angle,the evolution process from meso-angle and the evolution particulars from the micro angle,so as to better analyze the complicated topics evolution process. The proposed method is applicable only to scientific papers including key words and needs to be expanded to include other data resources.
The practical significance lies in:1)Proposing the dynamic topics identification method to provide reference to related research；2)Proposing a multidimensional topics evolution analysis model and an innovative visualization method which can be used to analyze the topics evolution rules and discover scientific and technological knowledge,etc；3)The findings can be used in scientific and research management to support decision-making,raise research efficiency and help to promote the scientific and technological innovation in related fields. 9 figs. 4 tabs. 37 refs.