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<p>To achieve the training goal of applied innovative talents, this paper designs an innovative model of training applied art and design talents based on environmental psychology to improve the quality level and practical ability of art and design talents. The k-means algorithm is improved by introducing a particle swarm algorithm, using information entropy to verify the effect of the clustering. The improved algorithm is used in developing the talent training model to make the development results more reasonable. Based on the strength of the training performance of each classifier in the random forest, the individual classifiers are weighted to reduce the influence of the poorly performing classifiers on the talent training quality evaluation results. A multi-level fuzzy evaluation model is used to design a quality evaluation index system for applied talent training, which is used to measure the merits of the talent training model. To verify the feasibility of the designed model, the simulation analysis results show that the assessment achievement on course objectives 1-5 reached 0.6784, the average score on design content, methods and ideas was 4.34, and the comprehensive score of talent training quality evaluation was 85.64. It shows that environmental psychology can support the cultivation of applied art and design talents and promote the overall development of applied talents.</p>
</abstract>

To achieve the training goal of applied innovative talents, this paper designs an innovative model of training applied art and design talents based on environmental psychology to improve the quality level and practical ability of art and design talents. The k-means algorithm is improved by introducing a particle swarm algorithm, using information entropy to verify the effect of the clustering. The improved algorithm is used in developing the talent training model to make the development results more reasonable. Based on the strength of the training performance of each classifier in the random forest, the individual classifiers are weighted to reduce the influence of the poorly performing classifiers on the talent training quality evaluation results. A multi-level fuzzy evaluation model is used to design a quality evaluation index system for applied talent training, which is used to measure the merits of the talent training model. To verify the feasibility of the designed model, the simulation analysis results show that the assessment achievement on course objectives 1-5 reached 0.6784, the average score on design content, methods and ideas was 4.34, and the comprehensive score of talent training quality evaluation was 85.64. It shows that environmental psychology can support the cultivation of applied art and design talents and promote the overall development of applied talents.

Model Innovation Research of Cultivating Applied Art and Design Talents Based on Environmental Psychology

Applied Mathematics and Nonlinear Sciences

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

To achieve the training goal of applied innovative talents, this paper designs an innovative model of training applied art and design talents based on...

Model Innovation Research of Cultivating Applied Art and Design Talents Based on Environmental Psychology

Data publikacji: 07 paź 2023

Zakres stron: -

Otrzymano: 14 gru 2022

Przyjęty: 17 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00512

Słowa kluczowe
Environmental psychology, K-means algorithm, Fuzzy evaluation model, Particle swarm algorithm

© 2023 Xiang Yue, published by Sciendo

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

Model Innovation Research of Cultivating Applied Art and Design Talents Based on Environmental Psychology

Data publikacji: 07 paź 2023

Zakres stron: -

Otrzymano: 14 gru 2022

Przyjęty: 17 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00512

Słowa kluczoweEnvironmental psychology, K-means algorithm, Fuzzy evaluation model, Particle swarm algorithm

© 2023 Xiang Yue, published by Sciendo

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

Słowa kluczowe
Environmental psychology, K-means algorithm, Fuzzy evaluation model, Particle swarm algorithm