###### Abstract  
By analyzing students' understanding of a certain subject's knowledge and learning process, and evaluating their learning level, we can formulate students' learning plans and teachers' curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students' learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students' achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students' learning levels. s +
Footnote †: journal: Medical  
system course during the undergraduate period. The results prove that there is a positive correlation between students' knowledge level and academic performance. Therefore, increasing the evaluation parameters in the evaluation system can effectively improve the evaluation accuracy and reliability of the system. Through the correlation analysis and mining of different parameters, the relevant factors affecting students' knowledge levels are found, and the evaluation results can help teachers make unique teaching plans for students with different grades [7; 8], and to provide constructive suggestions for students' learning and teaching decisions. Secondly, by analyzing the relevant factors that affect students' knowledge levels and inputting them into the machine learning model, data modeling is established to predict students' knowledge levels. Combined with the characteristic variables analyzed, this helps teachers identify the weaknesses of students and intervene in the learning process in advance, to effectively improve the learning level of students [9]. The most important part of this is the mining of student data. Data mining refers to the discovery of potential relationships and knowledge in massive student learning data. This process generally includes data acquisition, preprocessing, mining, and evaluation. To process data more quickly and accurately, artificial intelligence machine learning is used to achieve the corresponding goals. The machine learning model is trained and adjusted through the limited data collected, and seeks to discover internal relationships amongst the input parameters in the evaluation system and establish an evaluation system with high precision and reliability. It then seeks to provide more information to guide the development of students [10; 11]. At present, some scholars have used machine learning models to evaluate students' learning abilities. [PERSON] et al. [12] assessed the applicability of e-learning to developing strategies to improve students' readiness. Combining the ADKAR model with a machine learning-based feature importance recognition method, an effective method to assess the readiness of online learning is proposed. Two machine learning algorithms, random forest (RF) and decision tree (DT), are used to capture nonlinear relationships between data and improve the flexibility of data-driven models to effectively identify ADKAR factors that have the greatest impact on e-learning readiness. It is confirmed that ability and knowledge are important factors affecting e-learning readiness through experimental testing.