Research on Digital Model Construction of Differentiation Strategy of Applied Undergraduate Specialty Setting Based on Random Forest and Gaussian Naive Bayes

As the 21st century witnesses the expansion of university enrollment and the popularization of education, the traditional model of elite education is progressively evolving towards a model of mass education. This shift not only presents new challenges to the existing educational systems and pedagogical approaches but also urgently calls for the development of application-oriented undergraduate education. This paper, based on an analysis of the educational market and student needs, proposes a differentiation strategy for the setting of majors in applied undergraduate programs. Utilizing Random Forest and Gaussian Naive Bayes algorithms, a digital model is constructed to optimize the allocation of educational resources and enhance the quality of education, thereby improving students' employability. The study reveals that the degree of curriculum-industry integration, the proportion of practical teaching, the ratio of dual-qualified teachers, and the extent of enterprise cooperative courses are pivotal factors influencing the quality of major settings. This research not only offers a novel analytical tool for the configuration of majors in applied undergraduate education but also assists educational policymakers in achieving an effective alignment between educational content and market demands.