Implementation of Early Warning Algorithm for the Achievements of Foreign Students in Colleges and Universities Based on Grey Theory Model

With the increase in the number of college graduates employed, employment data and course performance data are also increasing exponentially. Under certain circumstances, there are certain rules between these data, which have certain guiding significance for the study, graduation and employment of school students and future students. Academic early warning is an important means to strengthen school academic management, improve students’ ability of self-management and self-discipline, and form a joint management mode between schools and parents. The grey prediction model shows strong adaptability in the information mining of the laws contained in complex systems. In order to further enhance the prediction performance of the model, this paper focuses on the improvement of modeling ideas and methods, and the construction method of grey system prediction model. The results show that among the calculation students, those who have put forward early warning account for 60.33% of the students who cannot graduate normally. By collecting a variety of behavior data generated by college students in campus activities, including consumption data, access control data, Internet data, etc., a student behavior data set is constructed. It proves that the proposed method can effectively improve the accuracy of College Students’ performance early warning.