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

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...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 11; p. 6818
Main Authors Zhang, Daopan, Wang, Sihua
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Back propagation networks
BPNN network
Collaboration
collaborative innovation centers
Colleges & universities
Decision making
Education
Entropy
Innovations
k-means
Neural networks
performance analysis
Performance evaluation
random forest
School construction
Jiangsu China
China
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13116818