In recent years, music has been developing rapidly in China and has become one of the hot items of current school teaching, and more and more schools have carried out college music education. However, there are still many problems that need to be solved in college music education, among which the problem of unreasonable teaching methods is particularly prominent. In this study, we use federal reinforcement learning technology to explore in depth the theory and method of the situational classroom teaching model and implement it into music education in China’s general colleges. Through empirical testing, we further analyze the optimization effect and empirical variability of the model to provide reference for the implementation and promotion of the situational classroom teaching model in China’s general college music education. The experimental results proved that the optimized federal average algorithm achieved better results than the traditional teaching method, using the situational classroom teaching method in college music education, which could improve the quality of set completion and significantly improve the teaching quality, and the accuracy of the optimized model of federal learning increased by 8%.