Enhancing Decision-Making in Art Education Through a Genetic Algorithm-Optimized K-Means Clustering Model

With rapid IT advances, new opportunities exist to enhance decision-making in art education. This paper proposes a data-driven model integrating intelligent signal processing and genetic algorithm optimization to improve teaching and enable personalized learning. The model uses genetic algorithms to optimize key signal processing functions like data manipulation, storage, and filtering, built on digital arithmetic units and small-scale circuits. A Genetic Algorithm-Optimized K-means clustering model was developed to incorporate big data into instruction, transforming traditional teacher-centered methods into dynamic student-centered environments. The system aligns with Decision Making Support Systems through data/model management, user-system interaction, and output presentation. Experimental results show enhanced teaching outcomes and student engagement, illustrating how intelligent technologies can support educational decisions. This study contributes to Intelligent Decision Support Systems by offering an innovative framework for talent development in creative disciplines.