Design and Application of Music Cloud Teaching Platform based on Artificial Intelligence Algorithm

The rapid evolution of intelligent education platforms has greatly transformed traditional teaching methodologies, especially in music education. However, existing digital platforms do not offer adaptive, efficient, and context-sensitive learning experiences. To address these limitations, this research develops an adaptive cloud-based music education system powered by a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. By modeling the sequential and temporal dependencies of musical elements, the proposed system supports real-time analysis of learner interaction patterns and enhances intelligent content delivery. The dataset used in this study comprises a diverse collection of user interaction data, audio features, and performance evaluations gathered from an online music learning environment. The BiLSTM model was employed to predict user skill levels and generate personalized learning paths. Experimental results demonstrated an accuracy of 98.2%, outperforming other learning models and confirming its effectiveness in addressing individual learner needs. Additionally, Precision (97.8%), Recall (98.1%), Specificity (97.9%), and F1-Score (97.9%) highlight the efficiency of the proposed method. The model is deployed within a cloud-based infrastructure, enabling seamless access to educational content, real-time monitoring of learner performance, and scalable delivery across institutions. This research represents a significant advancement in intelligent music education management, offering a robust and adaptive AI-powered learning environment suitable for large-scale academic deployment.