International student education is an important part of the internationalization of Chinese higher education, and the promotion of “The Belt and Road Initiative” has put forward higher requirements for the enrollment of international students studying in China, and it is of great significance to improve the quality of enrollment. In this paper, we extracted the data related to the enrollment and academic performance of international students from the academic affairs management system of each school and then pre-processed the data through data cleaning, normalization and desensitization. Then, we use the Apriori and C4.5 decision tree algorithm to mine the association rules between different influencing factors and the enrollment quality of international students and apply the decision tree to assist the decision-making of enrollment. Subsequently, the enrollment management system is constructed to realize the path of artificial intelligence technology to help improve enrollment quality. The results of association rule mining show that there are multiple association rules between different influencing factors and enrollment quality (with a minimum confidence level of 80%) and that international students’ intercultural communication ability and academic performance should be considered in the enrollment process. The empirical application of the enrollment management system found that the ideological quality of international students at the enrollment survey increased from 4.017 scores in 2020 to 9.221 scores in 2022, which verified that the enrollment system is an effective path to improve the enrollment quality of international students. The enrollment management system proposed in this paper can effectively provide decision-making reference and help for the enrollment of international students coming to China, and provide an effective path to improve the quality of international student enrollment in each school.