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.

随着数字技术的不断发展，音乐教学正逐渐从传统的面对面教学转向在线教学和智能学习方式。开发音乐教学软件对于改善用户的音乐学习方法和学习体验具有重要作用。但目前的音乐教学软件仍然存在兼容性差、数据安全和隐私保护水平较低、功能需求仍处于早期阶段等问题。本文旨在研究开发一款基于神经网络算法和用户分析的音乐教学软件，以提高用户对音乐教学软件的满意度。文章首先分析了系统的总体设计模块，然后利用聚类算法对音乐教学资源数据进行采集和归一化，以提高音乐教学资源数据的有效性。随后，采用并行结构的BP（Back Propagation）神经网络模型对神经网络模型进行优化设计，并分析该神经网络模型在用户界面设计中的有效性。最后，通过构建用户画像特征数据集，实现用户行为分析和个性化推荐。为了验证所开发的音乐教学软件的性能，对软件的性能和安全性进行了测试。结果表明，本文测试用例1中乐谱数据所需加载时间仅为1.21 s，响应时间仅为0.015 s。此时安全性为92.6%；音频数据所需的加载时间仅为1.06秒。响应时间仅为0.019s，安全性88.7%；用户交互数据的加载时间仅为0.78秒。响应时间仅为0.012 s，安全性为91.3%。研究结果表明，本文开发的音乐教学软件能够更好地满足用户的音乐教学需求，为用户提供更加智能、个性化、高效的音乐学习体验。