A Method for Predicting the Result of University Employment Education Transformation Based on Multi-Source Information Technology

Abstract

In the data on the outcomes of higher education employment transformation, there is high correlation among multiple independent variables, leading to multicollinearity. If multicollinearity exists, it can complicate the establishment of predictive analysis models and the interpretation of relationships between variables for higher education employment transformation outcomes. The employment issues of college graduates have become a focal point of social concern. These issues not only reflect the employment status of college graduates but also indirectly indicate the quality of higher education and the level of talent cultivation. Therefore, a prediction method for the effectiveness of higher education employment transformation based on multi-source information technology is proposed. According to the theory of multi-source information technology, an employment data classification framework is constructed to integrate multi-source information. The matching operator function output values are calculated, weights are determined, and the final college student information collection results are output. The standard error between employment unit types and salary ranges is analyzed, clustering training samples are trained, and the distance from each employment information sample to the centroid is calculated to obtain preprocessing results of the transformation outcomes, thus avoiding the impact of multicollinearity on the results of higher education employment transformation. A model structure for analyzing factors influencing higher education employment is established, allowing for an understanding of the characteristics of different types of educational resources and enabling the prediction of the effectiveness of higher education employment transformation. Experimental results show that this method can accurately monitor the dynamic employment situation of university students. After applying this method, the standard error and probability error values are significantly reduced, proving to be significantly effective in predicting the outcomes of higher education employment transformation.