Information System Construction and Prediction Model of Leaving Using Big Data Mining and Random Forest

Abstract

University talents play an important role in building the core competitiveness of universities and play a decisive role in improving the quality of university education. With the opening up and progress of society, the flow of talents in colleges and universities has become more and more frequent. Therefore, more and more attention has been paid to the analysis of factors affecting the turnover of outstanding college teachers. This paper establishes a random forest model to find out the key factors affecting the turnover of outstanding teachers in colleges and universities, and integrates these variables into an index to help colleges and universities understand which outstanding teachers need to be focused and predict the outstanding teachers on the job, and judge the probability of their departure., Which can formulate effective talent retention measures for the university managers. The experimental results verify that the work itself, work pressure and welfare remuneration are the most important factors affecting the turnover tendency of college teachers, followed by leadership management and development prospects. Relatively speaking, the impact of interpersonal relationships is the least. The evaluation model of university teachers’ turnover intention based on the random forest model proposed in this paper can be further applied to the study of teacher turnover intention.