Big Data Analysis of Digital Music Resources Based on Deep Learning

With the rapid growth of Internet, the problem of data overload has become increasingly prominent. In this article, a digital music resource recommendation model combining deep learning (DL) and collaborative filtering (CF) algorithm is proposed to mine the characteristics of users' music preferences, so that the recommendation system can accurately and effectively reflect users' personal preferences, and adjust according to users' preferences to achieve individualization recommendations for different users. The simulation results show that the accuracy of this music resource recommendation algorithm is improved by 24.64% compared with the traditional recommendation method. This model can not only extract time series features, but also be lightweight and high performance, and can more accurately classify songs for subsequent recommendation models.