Abstract: By leveraging facial expression recognition and emotion analysis,observers can understand learners' learning outcomes through the observed physical states.For instance,fluctuations in students' emotions displayed in the classroom can be used to discern their level of acceptance of new knowledge,facilitating a more convenient and intuitive understanding of students' confusion.However,in many cases,students' faces may be obstructed by learning materials,classmates in the front row,etc.,leading to inaccuracies in facial emotion recognition.Compared to the entire face,the eye region,as a core area of emotional expression,typically receives more attention from observers,and in the same classroom environment,the eyes are less likely to be obstructed.The eyes are one of the most important parts for displaying emotions,and changes in eye expressions during emotional fluctuations can provide more emotional information.Especially when a person is under external pressure and tend to suppress facial expressions,it is difficult to deceive with the gaze.Therefore,recognizing and analyzing complex eye expressions for emotions holds significant research value and challenges.To address this challenge,firstly,a dataset for classifying complex emotions in eye expressions is constructed,including five basic emotions,as well as defining five complex emotions.Secondly,a novel model is proposed to accurately classify emotions based on eye features extracted from input images in the dataset.Finally,a visualization method for emotion analysis based on eye recognition is introduced,which can analyze fluctuations in complex and basic emotions.This method provides a new solution for further eye-based emotion analysis.

Key words: Wisdom classroom, ConvNeXt, Data visualization, Emotion recognition, Eye area