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<p>This paper combines big data technology to analyze the training mode of digital media professionals in universities in various regions and explores the knowledge system, enrollment target, learning content, textbook construction, and employment direction of digital media professional training, respectively. The real data in the faculty management system is selected as the evaluation data, and the K-modes clustering algorithm is used to optimize the evaluation data in three problems, such as the number of clusters K, the initial clustering center, and the calculation of distance similarity. According to the research content, the evaluation index system of teachers’ teaching conditions in Y school was established, and the system was applied to evaluate the teaching operation of Y school comprehensively. The results showed that only 6.89% of the teachers were evaluated as “excellent”, 46.89% were evaluated as “good”, and 46.89% were evaluated as “medium”. The percentage of those who rated “medium” or below was 45.89%. This indicates that the university still needs to do a lot of work in improving teachers’ ability to meet the needs of college students in the new era. The application of the K-modes algorithm in the evaluation of digital media art professionals’ training is of great significance to the development of the digital media industry.</p>
</abstract>

This paper combines big data technology to analyze the training mode of digital media professionals in universities in various regions and explores the knowledge system, enrollment target, learning content, textbook construction, and employment direction of digital media professional training, respectively. The real data in the faculty management system is selected as the evaluation data, and the K-modes clustering algorithm is used to optimize the evaluation data in three problems, such as the number of clusters K, the initial clustering center, and the calculation of distance similarity. According to the research content, the evaluation index system of teachers’ teaching conditions in Y school was established, and the system was applied to evaluate the teaching operation of Y school comprehensively. The results showed that only 6.89% of the teachers were evaluated as “excellent”, 46.89% were evaluated as “good”, and 46.89% were evaluated as “medium”. The percentage of those who rated “medium” or below was 45.89%. This indicates that the university still needs to do a lot of work in improving teachers’ ability to meet the needs of college students in the new era. The application of the K-modes algorithm in the evaluation of digital media art professionals’ training is of great significance to the development of the digital media industry.

Exploration of digital media art professional design talent class training based on K-mode algorithm

Applied Mathematics and Nonlinear Sciences

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

This paper combines big data technology to analyze the training mode of digital media professionals in universities in various regions and explores the...

Exploration of digital media art professional design talent class training based on K-mode algorithm

Publié en ligne: 02 oct. 2023

Pages: -

Reçu: 10 nov. 2022

Accepté: 12 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00439

Mots clés
K-mode algorithm, Clustering center, Distance similarity, Digital media talent, Evaluation index

© 2023 Zongyan Huang, published by Sciendo

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

Exploration of digital media art professional design talent class training based on K-mode algorithm

Publié en ligne: 02 oct. 2023

Pages: -

Reçu: 10 nov. 2022

Accepté: 12 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00439

Mots clésK-mode algorithm, Clustering center, Distance similarity, Digital media talent, Evaluation index

© 2023 Zongyan Huang, published by Sciendo

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

Mots clés
K-mode algorithm, Clustering center, Distance similarity, Digital media talent, Evaluation index