<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper combines knowledge meta-extraction and knowledge fusion to construct a cognitive mapping model of party building work in colleges and universities, and the system contains four subsystems: data resource management, mapping ontology modeling, mapping construction, and mapping application. The content of the cognitive mapping is further optimized through the continuous collection, supplementation and improvement of subject knowledge data and the deduction, inference, induction and integration of party-building work using the cognitive mapping data model. After cognitive arbitrariness, the P-values of party-building organizational strength, intelligent party-building, branch activities, and organizational security are all less than 0.5, with significant positive effects. In the work dimension, the scores can reach 97 and 95. The number of participants in party-building organizational activities exceeded 500 after the innovation and even reached a maximum of 900. The results of the study can provide specific practical guidance for party-building work in colleges and universities, which can help them achieve better results in party-building work.</p>
</abstract>

This paper combines knowledge meta-extraction and knowledge fusion to construct a cognitive mapping model of party building work in colleges and universities, and the system contains four subsystems: data resource management, mapping ontology modeling, mapping construction, and mapping application. The content of the cognitive mapping is further optimized through the continuous collection, supplementation and improvement of subject knowledge data and the deduction, inference, induction and integration of party-building work using the cognitive mapping data model. After cognitive arbitrariness, the P-values of party-building organizational strength, intelligent party-building, branch activities, and organizational security are all less than 0.5, with significant positive effects. In the work dimension, the scores can reach 97 and 95. The number of participants in party-building organizational activities exceeded 500 after the innovation and even reached a maximum of 900. The results of the study can provide specific practical guidance for party-building work in colleges and universities, which can help them achieve better results in party-building work.

Exploration of Innovative Practical Path of Party Building Work in Colleges and Universities Based on Cognitive Mapping Construction

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper combines knowledge meta-extraction and knowledge fusion to construct a cognitive mapping model of party building work in colleges and universities,...

Exploration of Innovative Practical Path of Party Building Work in Colleges and Universities Based on Cognitive Mapping Construction

Online veröffentlicht: 11. Dez. 2023

Seitenbereich: -

Eingereicht: 08. Jan. 2023

Akzeptiert: 21. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01439

Schlüsselwörter
Knowledge meta-extraction, Cognitive mapping, Party building in colleges and universities, Data resource management, Ontology modeling

© 2023 Lixia Weng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploration of Innovative Practical Path of Party Building Work in Colleges and Universities Based on Cognitive Mapping Construction

Online veröffentlicht: 11. Dez. 2023

Seitenbereich: -

Eingereicht: 08. Jan. 2023

Akzeptiert: 21. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01439

SchlüsselwörterKnowledge meta-extraction, Cognitive mapping, Party building in colleges and universities, Data resource management, Ontology modeling

© 2023 Lixia Weng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Knowledge meta-extraction, Cognitive mapping, Party building in colleges and universities, Data resource management, Ontology modeling