Evaluation of Art and Design Talent Cultivation based on Time Series Ensemble Learning Techniques

Traditional approaches to evaluating talent in art and design have transformed considerably due to the rapid progress of big data and artificial intelligence (AI) technologies. This research explores a method driven by data for assessing the quality of talent development within the art and design sector. A more comprehensive and unbiased evaluation framework is proposed by applying AI techniques such as Deep learning include LSTM and GRU, along with large datasets of creative and educational achievements. To enhance decision-making in educational policies, the research illustrates how data analysis can uncover hidden trends in student performance, creativity indicators, curriculum efficiency, and the impact of educators. . The recommended approach achieves the maximum accuracy in the evaluation of art and design talent cultivation, according to the performance metrics, with an accuracy score of 98.12%, a precision score of 98.12%, a recall score of 98.11%, and an F1 score of 98.12%. The study also highlights how intelligent systems can be used to predict future developments in art and design education and to customize learning experiences for each student.