## 5 Conclusions  
To accurately assess the levels of learning mastery by students after systematic learning, the key variables that have the greatest impact on performance have been identified. This provides a theoretical basis for educators to customize teaching plans and allocate teaching resources. This study developed and improved upon a predictive model based on machine learning systems. The prediction model was constructed using the data from various aspects of a student's learning process, and three algorithms with increasing complexity were proposed to optimize the SVM model. The collected student learning information was used to construct a dataset, 70% of which was used for model training, and the other 30% of which was used to test the prediction effect of the model. By comparing the prediction effects of the four models, the advantages and disadvantages of the four models can be accurately evaluated. According to the research results, the following conclusions can be drawn:  
* Experiments show that the SSA, ISSA, and KPCA-ISSA compound algorithms can effectively improve the adaptability of the SVM model to data after multiple iteration optimization. According to the data, the ISSA algorithm has the lowest adaptation value of 0.0245 after four iterations;
* The prediction accuracies of the four models can be found. The results of the SVM model training stage produced an overfitting phenomenon, and the accuracy values of the training stage and test stage were 96.0584 and 91.8033, respectively. However, as the complexity of the composite model increased, the problem of overfitting was solved. The accuracy values of the KPCA-ISSA-SVM model in the training stage and test stage were 96.7972 and 96.7213, respectively. Therefore, improving the complexity of the model can effectively reduce the phenomenon of underfitting or overfitting;
* The importance experiment was used to explore the key variables that had the greatest influence on the prediction of students' learning levels. Regarding the data, the importance scores of PEG and LPR were 9.5958 and 4.2821, respectively; far greater than the other three input variables (STR, STG, and SCG). This means that the model will be biased towards PEG and LPR changes when predicting and evaluating students' learning levels, thus affecting the predicted results. Therefore, more attention should be paid to the PEG and LPR parameters of students when using UNS to evaluate students and specify study plans. The main purpose of this research was to effectively evaluate the learning levels of students by developing a system based on machine learning models. Through the integration and analysis of a large amount of data on student performance, educators can effectively classify students and make relevant teaching plans based on the model prediction results. This study is helpful in rationally allocating learning resources and making targeted intervention measures for students, helping to achieve individualized teaching. However, in general, this study still has some shortcomings. One is that the diversity of the dataset used is not high enough. On the other hand, there is a lack of comparison between the models. This study only used the SVM model for optimization, so several research directions remain to be addressed in future research. The first is to expand students' data sources and improve the diversity of data types. Other factors affecting the learning process, such as economic conditions, learning motivation, and knowledge system, are included in the construction data employed by the learning model. On the other hand, a horizontal comparison of different models has been performed to find a more appropriate prediction model that will improve the prediction accuracy and reliability of the model. Conceptualization, S.W., H.W. and J.H. ; methodology, H.W. ; and J.H. ; software, J.H. ; validation, H.W. and Y.L. ; formal analysis, Y.L. ; investigation, S.W. and H.W. ; data curation, S.W. and H.W. ; writing--original draft preparation, Y.L. ; writing--review and editing, H.W.