Exploration on Automatic Recommendation System for Video Background Music Based on Machine Learning

As the demand for video production expands, creators aim to improve the speed and efficiency of background music selection through Internet technology. This article aimed to provide an intelligent background music recommendation system for video producers by utilizing machine learning algorithms. Specifically, based on deep learning technology and natural language processing technology, this paper explored how to extract keywords, emotion analysis, and other information from video voice features. Based on this, automated background music recommendation was implemented, and the effectiveness of various algorithms was verified through experiments. The final results indicated that the model proposed in this article can provide personalized and accurate background music recommendations for different types of videos in various situations. The research results of this article would provide new ideas and methods for the research of music automatic recommendation systems and are expected to be applied in fields such as video editing, advertising, and entertainment.