The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning

This work proposes a personalized music learning platform model based on deep learning, aiming to provide efficient and customized learning recommendations by integrating audio, video, and user behavior data. This work uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to extract audio and video features, while using multi-layer perceptrons to encode user behavior data. To further improve the recommendation accuracy, this work constructs a knowledge graph that integrates key entities and their relationships in the music field, and fuses them with the extracted feature vectors. The knowledge graph provides the platform with rich semantic information and relational data, helping the model better understand the correlation between user needs and music content, thereby improving the accuracy and personalization of recommendation results. Experimental analysis based on different datasets shows that the proposed music recommendation platform performs well in multiple key performance indicators. Especially under different TOP-K conditions, the accuracy reaches 0.90, significantly exceeding collaborative filtering and content-based recommendation methods. In addition, the platform can maintain high accuracy when processing sparse data, demonstrating stronger robustness and adaptability. The platform has significant advantages in overall performance, providing users with more reliable and efficient recommendation services.