International Journal of Web Applications ShenghuanZhang, Lihong Xu 16 4 2024 https://doi.org/10.6025/ijwa/2024/16/4/143-150 https://www.dline.info/ijwa/fulltext/v16n4/ijwav16n4_4.pdf This article aims to introduce a new type of cloud music education platform which can meet the current high demand for music education while providing convenient, economical, and personalized services. The platform uses the Scrapy crawler framework, Web frontend, and Laravel framework and achieves efficient services through data interaction and embedded development. Using a hybrid recommendation algorithm, we can more accurately estimate the association between users and specified objects and decide whether to use this method by comparing their similarities. Through this work, we have achieved significant results in building a complete cloud music course experience, including a prediction accuracy of 8.41%, a recommendation effect of 10.21%, and a preference classification prediction accuracy of 2.55%.