As a whole, the quality of higher education instruction has grown in importance in recent years due to society’s fast pace of change. The extent to which instructors are able to enhance their teaching practices is influenced by the veracity and accuracy of the teaching quality evaluation. Accurate and genuine evaluation of faculty performance in higher education is an issue that traditional assessment techniques cannot address. Thus, a k-means clustering approach is suggested for quality analysis education. To begin, the interfering factor’s impact on teaching quality is diminished via computer-assisted analysis and evaluation; indications are then classified according to the standards for teaching quality. Afterwards, the computer assesses the quality of higher education instruction, develops a strategy to improve instruction, and analyses the outcomes comprehensively. Simulations conducted in MATLAB demonstrate that the k-means clustering algorithm assesses the quality of higher education instruction based on predetermined assessment criteria. Teaching quality assessment outperforms more conventional forms of evaluation in terms of validity and reliability.