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

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.

Keywords

• environmental psychology
• k-means algorithm
• fuzzy evaluation model
• particle swarm algorithm
• 91e45