Performance Analysis of University Collaborative Innovation Center Based on BPNN-Dominated K-Means–Random Forest Unsupervised Factor Importance Analysis Model

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.