Intelligent recommendation algorithm for piano tracks based on the CNN model

Abstract Introduction In order to improve the effect of intelligent recommendation of piano repertoire, this article combines the convolutional neural network (CNN) model to perform intelligent analysis of piano repertoire and combines personal preference for piano repertoire recommendation. Objectives Moreover, this article conducts research on the identification of piano waveform features in the process of piano practice by piano practitioners in complex situations. Methods In addition, this article conducts waveform extraction through hardware design, which provides a basis for piano preference identification, and theoretically analyzes the design of sealed resonant windows and the design of concentrated electric field strength resonant gaps. Results About 92.72% of the samples have a maximum ratio of more than 0.9, and the maximum ratio of the samples is greater than 0.5. That is to say, most of the errors still come from prediction and classification errors, and a small part of them are data errors. Finally, this article constructs an intelligent recommendation model for piano repertoire based on the CNN model after introducing the resonance element into the piano waveform characteristic factor limiter. Conclusion The proposed CNN-based recommendation algorithm demonstrates high accuracy and effectively enhances piano repertoire recommendations.