Construction of Western Music Theory Teaching Model Based on Machine Learning

Western wind music is an important content for music majors to learn, through learning western wind music can improve students’ expressive ability, enrich students’ music content, and let students get a higher level of development. The research is based on machine learning to mine and process the western music theory teaching data and theory teaching performance, and 13 feature values are obtained after processing the western music features, and the correlation analysis is carried out by collecting the related data of 594 students majoring in music in a university, and the correlation analysis is carried out on the student’s performance, the one card, the library and the student’s behavioral data, and then logistic regression method is applied to obtain the coefficients of the behavioral features and analyze the results. The behavioral characteristics with strong correlation with Western music learning were analyzed. The study shows that the number of students’ book borrowing is significantly correlated with the average course theory grade, followed by the coefficient results based on the characteristics show that the weight value of independent learning ability is 0.47, which is the behavioral characteristic with the highest correlation force. Based on the results of the study based on blended learning theory to build a Western music theory teaching model, which helps to provide better personalized learning services and improve the effectiveness and quality of students’ music learning.