Application of Convolutional Neural Network in Emotion Recognition of Ideological and Political Teachers in Colleges and Universities

With the update of Internet technology and the development of we-media, ideological and political education in colleges and universities has been greatly impacted. Higher requirements are put forward for ideological and political teachers in colleges and universities, whose emotions seriously affect the quality and effect of teaching. Aiming at the problems of poor network generalization ability and large computation amount caused by many network parameters in the existing emotion recognition methods, a face emotion recognition method based on convolutional neural network is proposed. The network structure of nested Maxout multilayer perceptron layer is constructed by optimizing the convolutional neural model. Maxout can enhance the feature extraction capability of the convolutional layer of convolutional neural network. Meanwhile, Maxout performs linear combination of target features to select the most effective feature information. Then, the pretraining model is used for emotion recognition training. The strong perception ability of the model for facial features is retained by changing the important parameters. Simulation results demonstrate that this method has a higher recognition rate of face emotion and can effectively achieve accurate face emotion classification.