The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning

This exploration aims at solving multiple teaching problems in piano online education course. On the premise of collaborative filtering, the K-means clustering algorithm is employed to apply the time data to the neural collaborative filtering algorithm, and the Improved Neu Matrix Factorization (Improved Neu MF) algorithm model is implemented. After the experiment, the relevant evaluation indexes are selected and the simulation test is operated on the relevant dataset. The test results show that root mean square error (RMSE) reaches 1.251 and mean absolute error (MAE) is 0.625. Indexes are adopted to evaluate the order of the model. The results suggest that the designed algorithm is better than the comparison algorithm, proving that the optimized model has better performance and can be used to construct an online course model. Based on deep learning, using the designed algorithm to build the online learning model of piano education can provide better, dynamic, and personalized online course recommendations for piano education. In this way, it can improve students' learning efficiency, promote the online learning development of piano education, and have vital practical significance for disseminating art and culture.