Application of Convolution Neural Network Algorithm in Online Education Emotion Recognition

Abstract

The setting of teaching environment is the key factor of teaching emotion recognition, and its superiority directly determines the teaching and learning effect between teachers and students. During online education, the changes of students' emotions are not paid attention to and addressed by teachers. Especially for young students, their self-study ability and self-discipline are poor, which further affects the learning. This paper proposes an improved convolutional neural network algorithm to create a decision tree model for managing students' scores. The experimental results show that the improved convolutional neural network algorithm improves the construction speed of the decision tree and reduces the calculation and execution time of the algorithm. The improved algorithm proposed in this paper has a good classification effect. The model provides a reference for the expansion and application of emotion recognition big data in education and teaching, and a feasible practical model for personalized teaching in online schools.