Research on evaluation algorithm of teacher's teaching enthusiasm based on video

Most of the current research on teachers' teaching enthusiasm is to define teachers' emotions in a qualitative way, but lacks intuitive digital or image representations. Based on the online teaching platform, this research collects various types of teaching videos, and establishes a teacher's teaching emotion data set based on the PAD emotion model, then extracts teacher's teaching features through various algorithms such as sound feature extraction, facial expression recognition, target detection, and pose estimation. Analyze the features of different modalities, and cascade the features based on the contribution of each feature to the result. Finally, use BP neural network to predict teachers' enthusiasm. The study found that the model prediction performance is better after adopting the cascading feature fusion method based on feature contribution. The loss of the P prediction model is 0.0586, the loss of the A prediction model is 0.0517, the loss of the D prediction model is 0.0428, and the average loss is 0.0510.