Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model

The traditional graduate admission method is to evaluate students’ performance and interview results, but this method relies heavily on the subjective feelings of the evaluators, and these methods may not be able to comprehensively and objectively evaluate the qualifications and potential of the applicants. At present, artificial intelligence has played a key role in the reform of the education system, and the data processing function of artificial intelligence has greatly reduced the workload of screening work. Therefore, this study aims to optimize the graduate enrollment evaluation process by applying a new composite model, the random forest–improved sparrow search algorithm (RF–ISSA). The research used seven data sets including research, cumulative grade point average (CGPA), letter of recommendation (LOR), statement of purpose (SOP), university rating, TOEFL score, and graduate record examination (GRE) score, and carried out the necessary data pre-processing before the model construction. The experimental results show that the RMSE and R values of the composite model are 0.0543 and 0.9281, respectively. The predicted results of the model are very close to the actual data. In addition, the study found that the importance score of CGPA was significantly higher than other characteristics, and that this value has the most significant impact on the outcome of the graduate admissions assessment. Overall, this study shows that combining the integrated strategy sparrow search algorithm (ISSA) with hyperparameter optimization and focusing on the most influential features can significantly improve the predictive performance and applicability of graduate admissions models, providing a more scientific decision support tool for school admissions professionals.