Performance Analysis of University Collaborative Innovation Center Based on BPNN-Dominated K-Means–Random Forest Unsupervised Factor Importance Analysis Model
Source abstract
Abstract
The collaborative innovation plan for colleges and universities is one of the important plans for the construction of high-level universities in Jiangsu Province. A key aspect of this plan is the development of collaborative innovation centers in colleges and universities. Based on the second-phase construction of collaborative innovation centers in 76 colleges and universities in Jiangsu Province, this paper constructs performance evaluation indicators and proposes an unsupervised factor importance analysis model based on Back Propagation Neural Network (BPNN)-dominated K-means and random forests. According to the analysis results, suggestions for further promoting the development of high-quality collaborative innovation centers in colleges and universities are provided.
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Research context
OpenAlex topics: Multi-Criteria Decision Making · Evaluation Methods in Various Fields
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Affiliations
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Scientific Research Department, Nanjing Audit University, Nanjing 211815, China
School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
References (22)
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