A method for improving the professional ability of art design teachers based on neural network model

The art and design major in colleges and universities (CAU) places high demands on teachers’ professional quality and ability. To meet these requirements, it is necessary to adopt concepts of diversity and advanced education, allocate teaching resources according to employment needs, and cultivate high-quality talents aligned with market development. Improving the effectiveness of talent training for art and design professionals therefore depends largely on enhancing teachers’ professional abilities. This paper proposes several methods to improve the professional ability of art and design teachers and evaluates their effectiveness using a Deep Belief Network (DBN) model. First, the research status of art design curricula and teachers’ professional abilities at home and abroad is reviewed. Second, strategies for improving teachers’ professional ability are proposed, and the DBN model theory and its application process are analyzed based on practical requirements. Third, simulation experiments are conducted using optimized parameters, with training and control data collected for comparative analysis to examine the model’s effectiveness. Experimental results show that, compared with traditional evaluation methods, the DBN-based model demonstrates clear advantages in enhancing the professional ability of art and design teachers, indicating strong potential for further application and promotion.