Artificial intelligence (AI) has emerged as a significant driving force in the development of technology and industry. It is also integrated with music as music AI in music generation and analysis. It originated from early algorithmic composition techniques in the mid-20th century. Recent advancements in machine learning and neural networks have enabled innovative music generation and exploration. This article surveys the development history and technical route of music AI, analyzes the current status and limitations of music artificial intelligence across various areas, including music generation and composition, rehabilitation and treatment, as well as education and learning. It reveals that music AI has become a promising creator in the field of music generation. The influence of music AI on the music industry and the challenges it encounters are explored. Additionally, an emotional music generation system driven by multimodal signals is proposed. Although music artificial intelligence technology still needs to be further improved, with the continuous breakthroughs in technology, it will have a more profound impact on all areas of music.

In evaluating classroom teaching quality, students’ attention assessment is a critical indicator in education management, as it holds significant practical value for improving teaching methods and instructional quality. Electroencephalogram (EEG) signals can monitor dynamic neural activity in the brain in real time. Their objectivity and non-invasive nature make them particularly suitable for attention assessment in classroom environments. This article first provides a brief overview of existing attention assessment methods, and then presents a comprehensive review of the current research status and methodologies in EEG-based attention assessment, including signal acquisition, preprocessing, feature extraction and selection, classification, and evaluation. Subsequently, the challenges in EEG-based teaching attention assessment are discussed, including the acquisition of high-quality signals, multimodal data fusion, complexity of data, and hardware setups for deep learning method implementation. Finally, a multimodal classroom attention assessment method, which integrates EEG and eye movement signals, is proposed to enhance teaching management.

Ecological sustainable development model is a green ecological model and a prerequisite for economic sustainable development. Higher education is vocational education and vocational training for relevant personnel, aiming to cultivate more professional talents and skilled workers for the country and society, so as to achieve the goal of social development. This paper aims to use advanced technical means to construct an ecological sustainable development model for higher education. Based on relevant computer technology, this paper proposes a support vector machine (SVM) model and a BP (back propagation) artificial neural network algorithm to analyze and study the ecological sustainable development model. Through experimental analysis, it is known that in the survey of teachers, 199 teachers believe that resources should be effectively utilized in the network environment. This part of teachers accounts for the largest proportion, which is 25.51%, while the proportion of teachers with academic theoretical knowledge is the smallest, which is 7.06%. This shows that teachers should strengthen technical learning. In the process of distance education, practical operation is far more important than theoretical knowledge, and teachers’ skill training also affects the sustainable development of education. Finally, the calculation results show that the experimental method has certain significance for the analysis of ecological sustainable development of higher education.