With the rapid development of music streaming media service industry, users can easily hear any song on mobile devices. Internet has become a huge music storage platform. With the development of network and large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music loved by users from the massive Internet digital music data has become the key problem and main goal to be solved in the field of music information retrieval. Personalized music recommendation system can accurately find and push songs that users may be interested in from tens of millions of huge music libraries according to users’ information under the condition that users only have vague demand for listening to songs. Relying on the traditional search method to find the music that you are interested in can no longer meet the needs of users, so the current music recommendation system needs to dig out the music that has no clear needs in the long tail to help people find their favorite songs.
While paying attention to students’ acquisition of knowledge, we should also pay attention to students’ ideological and moral education. Bayesian network is a probabilistic graphical model that was developed in the 1980s. Therefore, it is urgent to establish the correct outlook on life and values of the motherland in the future. Music is one of the main categories of aesthetic education, which not only plays a role in cultivating students’ talents and skills, but also has the function of moral education, so it has its special teaching position. It provides the means of knowledge representation, reasoning, and learning in uncertain environment. Based on Bayesian learning algorithm, this paper studies the moral education function of music art in college students. Bayesian network can effectively carry out multivariate joint prediction, causal reasoning, expression of uncertain knowledge, pattern recognition, image processing, and causal data mining. Among them, the method based on score search regards structural learning as a combinatorial optimization problem, and score function and search method are two important factors that affect the learning effect.
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