An Algorithm for Analyzing Psychological Characteristics in Music Education Based on Markov Model

In the stage of music teaching, students are the subject of teaching. Music courses especially emphasize the cultivation of students’ learning psychology. Therefore, in music teaching, teachers should understand students’ psychological state, guide and control students’ psychological process and cultivate their good psychological quality. Therefore, this article proposes a psychological feature analysis algorithm based on Markov model. The algorithm is applied to students’ psychological well-being assessment data, and the factors affecting students’ psychological well-being are analyzed. According to the mining results, students’ psychological well-being problems can be understood more deeply. Finally, the effectiveness of the algorithm is verified by simulation experiments. The simulation results show that the algorithm can correctly classify students’ psychological well-being, and its classification accuracy can reach 95.91%. And the accuracy of psychological crisis prediction is better and the average response time is shorter. Only by grasping the psychological changes of students can music teaching and education be better implemented. The application of the algorithm proposed in this article in music education can predict students’ psychological problems and provide some technical support for music teaching.