Analysis of the Multi-Angle Matching between ESI Selected Research Frontiers and University Academic Directions:A Case Study of Environmental/Ecological Disciplines at D University
[Purpose/Significance]Against the backdrop of global technological competition and education-al reform,higher education institutions need to keep pace with the forefront of international scientific research to achieve synchronized upgrading of their disciplinary development strategies.Taking the Environment/Ecology dis-cipline of Dalian University of Technology as a case study,the purpose of this research is to construct a systematic analytical framework aimed at accurately identifying ESI-selected research frontiers and assessing their alignment with the disciplinary directions of universities.The core significance of the study lies in promoting disciplinary in-novation and the improvement of educational quality,while also providing decision support for strategic planning in higher education institutions.The key issues addressed in this paper include:how to identify and connect disci-plinary development with the international scientific research frontier,and how to analyze the academic achieve-ments of the Environment/Ecology discipline at University D in depth,to explore the potential for disciplinary development,address challenges,and provide data-driven strategic recommendations for disciplinary construction and scientific research management.The aim is to assist universities in promptly grasping the trends of international scientific research,exploring emerging areas within disciplines,and optimizing their own disciplinary construction.[Method/Process]The study focuses on the ESI research fronts in environment/ecology and the corresponding discipline papers from University D over the past 10 years.Utilizing e Python,the study employs cutting-edge techniques such as text mining,machine learning,and graph analysis to conduct multidimensional analysis of dis-ciplinary alignment and differentiation.[Result/Conclusion]The parallel use of keyword matching,LDA(Latent Dirichlet Allocation)model,and knowledge graph analysis can serve the planning of ESI fronts and the strategic development of universities comprehensively and accurately,providing a reference for scientific research manage-ment.
ESI research frontspythonmachine learningknowledge graph
NETL
NSTL
万方数据
