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<p>This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific cultivation path for art design professionals under the industry-teaching fusion mode with respect to the cultivation value of industry-teaching fusion for art design professionals. The association rule algorithm is selected to set up art design professional courses, and the neural network algorithm is selected to establish the talent cultivation mechanism model of industry-teaching fusion under the background of big data. According to the association rules for talent training path effect prediction, and then use big data is used to analyze the demand characteristics of art design professional talent training under the fusion of industry and education based on the demand characteristics for targeted talent training. The average difference between the student’s scores and the actual scores is 17.38, the accuracy rate of the model is 87.15% in the case of the allowable error range of 10 points, and the accuracy rate of the model is 90.55% in the case of the allowable error range of 15 points. A series of interventions, such as promotion rate prediction and academic warning, can be implemented based on this.</p>
</abstract>

This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific cultivation path for art design professionals under the industry-teaching fusion mode with respect to the cultivation value of industry-teaching fusion for art design professionals. The association rule algorithm is selected to set up art design professional courses, and the neural network algorithm is selected to establish the talent cultivation mechanism model of industry-teaching fusion under the background of big data. According to the association rules for talent training path effect prediction, and then use big data is used to analyze the demand characteristics of art design professional talent training under the fusion of industry and education based on the demand characteristics for targeted talent training. The average difference between the student’s scores and the actual scores is 17.38, the accuracy rate of the model is 87.15% in the case of the allowable error range of 10 points, and the accuracy rate of the model is 90.55% in the case of the allowable error range of 15 points. A series of interventions, such as promotion rate prediction and academic warning, can be implemented based on this.

Exploration of Talent Cultivation Path for Art and Design Majors under Industry-Teaching Integration Mode Based on Big Data Analysis

Applied Mathematics and Nonlinear Sciences

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

This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific...

Exploration of Talent Cultivation Path for Art and Design Majors under Industry-Teaching Integration Mode Based on Big Data Analysis

Online veröffentlicht: 11. Nov. 2023

Seitenbereich: -

Eingereicht: 28. Dez. 2022

Akzeptiert: 22. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01082

Schlüsselwörter
Big data analysis, Association rule algorithm, Neural network algorithm, Industry-teaching integration, Talent cultivation, Art design

© 2023 Shaobo Deng, published by Sciendo

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

Exploration of Talent Cultivation Path for Art and Design Majors under Industry-Teaching Integration Mode Based on Big Data Analysis

Online veröffentlicht: 11. Nov. 2023

Seitenbereich: -

Eingereicht: 28. Dez. 2022

Akzeptiert: 22. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01082

SchlüsselwörterBig data analysis, Association rule algorithm, Neural network algorithm, Industry-teaching integration, Talent cultivation, Art design

© 2023 Shaobo Deng, published by Sciendo

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

Schlüsselwörter
Big data analysis, Association rule algorithm, Neural network algorithm, Industry-teaching integration, Talent cultivation, Art design