This paper focuses on the “Internet + education” model and describes the feasible strategy of using data mining technology to establish a teaching quality monitoring mechanism in colleges and universities. Through the study of teaching quality monitoring pathways, such as teachers’ teaching level, students’ academic status, and course learning effectiveness, this paper’s main content provides a directional guide. Based on the teaching quality monitoring pathway, we choose the related algorithms of data mining technology, such as cluster analysis, association rule, and factor analysis, to be applied to the various aspects of the teaching quality monitoring pathway. The clustering algorithm is used to classify 1500 teachers in a university into 4 categories, such as excellent teaching effect, good teaching effect, etc., as well as to cluster students into 6 categories, such as excellent in all the courses and low to medium level, according to the course grades. In addition, this paper analyzes students’ course learning effectiveness, academic level, and career guidance using association rule algorithms, factor algorithms, and decision tree algorithms, respectively. From teachers’ teaching to students’ learning and employment, this paper constructs an all-round teaching quality monitoring mechanism using data mining technology, which contributes to the improvement of teaching quality in colleges and universities.