Abstract: To address the difficulties faced by educators in monitoring and evaluating learners' diverse online learning behaviors, a MLP-Bagging ensemble classification model with an embedding layer is proposed to analyze and distinguish learners' online learning behaviors. Considering the online learning behavior and individual characteristics of learners, an online learning behavior model is constructed from five aspects: learning readiness behavior, knowledge acquisition behavior, interactive learning behavior, knowledge consolidation behavior, and auxiliary features. The MLP-Bagging ensemble classification model is used to classify and distinguish learners. The experimental results show that the constructed learning model can model the learning behavior of online learners in line with reality. Adding auxiliary features is conducive to in-depth analysis and guidance of various types of learners' online learning behavior, and adding an embedding layer in the classification model can effectively overcome the data redundancy and error defects caused by label encoding, thereby achieving better classification results. Compared with other classification models, the Bagging ensemble model that integrates multiple MLP classifiers can reduce the variance of a single MLP classifier, with a classification accuracy of 98.72%, and has good practical application value.