Academic early warning model based on machine learning and model application

With the expansion of higher education, the uncertainty of students' academic completion and the diversity of academic crises have posed new challenges to the management of higher education. This study aims to design and implement a dynamic academic early warning system based on machine learning to predict and intervene in students' academic crises. By analyzing the causes of academic crisis, the fuzzy comprehensive evaluation with analytic hierarchy process method is used to construct an academic early warning indicator system comprising 10 key indicators. This ensures the scientificity and rationality of the indicator system through expert scoring and consistency test. On this basis, a radial basis function neural network was used to construct an academic early warning model, which outperforms the recurrent neural network and Softmax regression model in terms of prediction accuracy and convergence speed. The system was developed using hypertext markup language, cascading style sheets, JavaScript, and Python to achieve a user-friendly human-computer interaction interface and provide personalized academic alert services. The experimental results demonstrate that the system exhibits high sensitivity and accurate recognition capabilities when dealing with large-scale student datasets, achieving an accuracy rate of 96.32% and a root mean square error of 0.2926, which meets the practical requirements of academic early warning. The results of this study not only provide a new academic early warning tool for colleges, but also have important practical value for promoting the construction of smart campus and digital campus. The current findings, drawn from a sample limited to engineering with an overrepresentation of males, require future validation in multi-disciplinary and gender-balanced cohorts to establish broader applicability.