## 5 Conclusions  
The primary objective of this study is to utilize artificial intelligence models for processing and analyzing data related to graduate admissions evaluations and to establish an evaluation model based on this analysis. Currently, commonly used prediction models typically rely on a single core model, which often lacks the necessary generalization capability when faced with unseen data. Therefore, in this research, the ISSA algorithm is utilized as a supplementary means by which to optimize the hyperparameters of the RF model, to improve the machine learning model's responsiveness to the correlation between graduate admissions outcomes and student-related data, and to further improve the model's predictive performance. By constructing a dataset from collected student data and admission outcomes, this dataset serves as the foundational training and testing data for the model. Statistical metrics are used to evaluate the model's strengths and weaknesses. Based on the research findings, the following conclusions can be drawn:  
Employing the ISSA algorithm for hyperparameter adjustment effectively enhanced the predictive capability of the RF model. The RMSE test data clearly show that, as the number of iterations increases, the gap between predicted results and actual data gradually decreases. This indicates a high degree of fit between the ISSA algorithm and the RF model, effectively leveraging the strengths of both. The ISSA-RF algorithm effectively reduces the impact of noise and outliers in the dataset. Despite the presence of some prediction values that significantly deviate from actual values in both the training and testing datasets, these anomalies have a limited impact on the model's overall predictive accuracy. The model demonstrates high predictive precision, with a statistical parameter R reaching 0.9281. Figure 7: Importance of input variables. The analysis of feature importance reveals that a student's CGPA has the greatest impact on graduate admissions outcomes. In contrast, the importance scores for research and TOEFL score are very low. This indicates that the most crucial assessment parameter for graduate admissions is the student's academic performance rather than extracurricular factors. Future admissions evaluations could consider modifying student feature indicators to include more specific in-school data rather than extracurricular achievements. Overall, the study demonstrates that incorporating ISSA for hyperparameter optimization and focusing on the most influential features significantly improves the predictive performance and applicability of the graduate admissions model. However, there are still many limitations in this study. First of all, this study only uses an ISSA single algorithm and model for combination and analysis, although it is clear from the experimental results that this algorithm has a high accuracy for this study. However, this does not mean that other algorithms are less capable than ISSA. On the other hand, the data used in this study come from a single source and may be flawed in the face of different problems. In future research, more kinds of algorithms and models will be combined and analyzed to find more excellent algorithms and models with higher accuracy. In addition, more and different kinds of data are collected to train the model to build a model that can be widely used in the field. Conceptualization, E.L., Z.W. and J.L. ; methodology, E.L., Z.W. and J.L. ; software, J.H. ; validation, E.L., Z.W. and J.L. ; formal analysis, E.L. and Z.W. ; investigation, E.L., Z.W. and J.L. ; writing--original draft preparation, E.L., Z.W. and J.L. ; writing--review and editing, E.L., Z.W., J.L. and J.H. ; supervision, J.L. and J.H. ; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.