In order to solve the occlusion problem of student expression recognition in complex classroom scenes， and give full play to the advantages of deep learning in the application of intelligent teaching evaluation，a student expression recognition model and an intelligent teaching evaluation algorithm based on deep attention network in classroom teaching videos were proposed. A video library， an expression library and a behavior library for classroom teaching were constructed， then， multi-channel facial images were generated by cropping and occlusion strategies. A multi-channel deep attention network was built and self-attention mechanism was used to assign different weights to multiple channel networks. The weight distribution of each channel was restricted by a constrained loss function， then the global feature of the facial image was expressed as the quotient of the sum of the product of the feature times its attention weight of each channel divided by the sum of the attention weights of all channels. Based on the learned global facial feature， the student expressions in classroom were classified， and the student facial expression recognition under occlusion was realized. An intelligent teaching evaluation algorithm that integrates the student facial expressions and behavior states in classroom was proposed， which realized the recognition of student facial expressions and intelligent teaching evaluation in classroom teaching videos. By making experimental comparison and analysis on the public dataset FERplus and self-built classroom teaching video datasets， it is verified that the student facial expressions recognition model in classroom teaching videos achieves high accuracy of 87.34%， and the intelligent teaching evaluation algorithm that integrates the student facial expressions and behavior states in classroom achieves excellent performance on the classroom teaching video dataset.