Music teaching software development based on neural network algorithm and user analysis

Abstract

With the continuous development of digital technology, music teaching is gradually shifting from traditional face-to-face teaching to online teaching and intelligent learning methods. Developing music teaching software plays an important role in improving users’ music learning methods and learning experience. However, current music teaching software still has issues such as poor compatibility, low level of data security and privacy protection, and functional requirements that are still in the early stages. This article aimed to research and develop a music teaching software based on neural network algorithm and user analysis, so as to improve user satisfaction with music teaching software. In the article, the overall design module of the system was first analyzed, and then music teaching resource data was collected and normalized using clustering algorithms to improve the effectiveness of music teaching resource data. Afterwards, a BP (Back Propagation) neural network model with parallel structure was used to optimize the design of the neural network model, and the effectiveness of the neural network model in user interface design was analyzed. Finally, user behavior analysis and personalized recommendation were achieved by constructing a user profile feature dataset. In order to verify the performance of the developed music teaching software, the software performance and security were tested. The results showed that the required loading time for the music score data in test case 1 of this article was only 1.21 s, and the response time was only 0.015 s. At this time, the security was 92.6%; the required loading time for audio data was only 1.06 s. The response time was only 0.019 s, and the security was 88.7%; the loading time required for user interaction data was only 0.78 s. The response time was only 0.012 s, and the safety was 91.3%. The research results indicated that the music teaching software developed in this article could better meet users’ music teaching needs and provide users with a more intelligent, personalized, and efficient music learning experience.