The quality evaluation of vocal music teaching for music majors is of great significance to music education. Based on the theory of multiple intelligences, this paper constructs a model for evaluating the quality of vocal music teaching for music majors and introduces the theory of multiple intelligences into the operation form, design requirements, and recommended lesson examples of music teaching unit design. The experimental data and questionnaire data collected by the model verify that this operation is beneficial to the improvement of students’ music listening scores, vocal music comprehension scores, and total scores and solves the quantitative problem of vocal music teaching quality evaluation. In the simulation process, the engineering testing and analysis method uses the correlation rate and correlation strength as the analysis indicators, comparing the platforms for the quality evaluation of vocal music teaching in music majors and the corresponding quality evaluation data preprocessing process. The experimental results show that the algorithm performance evaluation is carried out based on the three aspects of quality evaluation association rules, algorithm running time, and algorithm memory consumption. The multiple intelligence algorithm is applied to vocal music teaching analysis of association rules for quality evaluation: when 4000 tasks call for 20–40 virtual resources, the total time spent is reduced by 61.7%, which has a significant positive effect on the knowledge expansion and ability improvement of music majors.
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