Analysis of User Music Behavior and Emotion Technology in Intelligent Music System Design

With the popularity of smart music systems, users' music behaviors and emotional needs are receiving increasing attention. Understanding users' emotional responses in music selection and experience can help improve personalized recommendation and user experience, and thus promote the development of intelligent music technology. However, most existing studies focus on technical implementation and lack in-depth analysis of user behavior and emotion. In this paper, we use a combination of data analysis to collect a large amount of data on users' music behavior and emotional responses in different scenarios. By using an emotional computing model to categorize users' music preferences and combining it with machine learning algorithms, the relationship between users' emotional states and music choices is explored in depth. The results of the study show that there is a significant correlation between users' music choices and their emotional states, and there is a clear preference trend between specific emotions and specific music genres. Intelligent music system design should pay more attention to the user's emotional experience, and optimize the recommendation algorithm through a deeper understanding of the user's behavioral patterns, so as to provide a more personalized music experience.