Higher education has a major role to play in social progress, technology, and economic development. However, as the number of students enrolled in higher education increases, the quality of graduates is declining and their ability to work is being questioned. Therefore, it is very important to effectively evaluate the quality of teaching and learning. With this in mind, an evaluation approach using data mining based on wireless networks is proposed in this paper. In the educational work of universities, data mining helps to assist universities to improve management efficiency and teaching effectiveness in managing students by mining and analyzing large-scale educational data. In order to improve their teaching management, this paper uses the Apriori method among correlation rule mining methods. The algorithm is improved in terms of time efficiency, and then the improved algorithm is used to mine and analyze the impact of teachers’ age, title, educational background, and student performance on teaching quality evaluation. The results of the exploration showed that, through the mining algorithm, it was found that the student’s evaluation was proportional to the student’s performance. For example, the average grade was excellent, and most evaluation grades were excellent. Its support level was 26% and its confidence level was 62.5%, indicating that more than half of the students met its characteristics. It also showed that the results were real, and the quality of teachers’ teaching could be known through the results of students.