The Impact of Drawing Systems Based on Embedded Neural Network Computing on Student Mental Health

With the rapid development of artificial intelligence technology, embedded neural networks, as their core components, have shown enormous potential for application in multiple fields due to their powerful data processing and pattern recognition capabilities. In the field of painting education, the application of this technology can not only achieve customized teaching plans, but also intelligently analyze the characteristics of students’ behavioral works. This study focuses on the application of embedded neural network computing in painting teaching. Especially how to dynamically adjust the difficulty, style preferences, and interactive methods of teaching content to stimulate students’ creativity and learning interest. By intelligently analyzing students’ emotional reactions and psychological states, timely adjusting teaching strategies, such as providing emotional support, adjusting learning pace, or introducing psychological counseling content, can alleviate students’ learning pressure, and enhance their self-efficacy and sense of belonging. In addition, this technology can promote effective communication between teachers and students, establish more positive and harmonious teaching relationships, and create a more inclusive and encouraging learning environment for students. The research results indicate that compared to traditional painting teaching models, the teaching method based on embedded neural networks significantly improves the mental health level of students, which is manifested in the reduction of anxiety and depression, enhancement of self-confidence, and improvement of social skills.