Analysis and Evaluation of the Impact of Integrating Mental Health Education into the Teaching of University Civics Courses in the Context of Artificial Intelligence

Abstract

In higher education teaching work, college students not only need to master the professional knowledge and professional skills they learn during their school study but also need to improve their self-education and self-cultivation and constantly improve their comprehensive ability of learning. At present, there are differences and relationships between the education of college students in civic and mental normal education, and how to play the role of the integration of the two educations has become a problem that needs to be considered in the current work of college students’ training. The integration between civic education and mental normal education can make up for the shortcomings of monolithic civic education and mental normal education work and also optimize the teaching methods between them from a certain perspective to achieve the development goals of complementing each other and not being independent of each other, so that students can understand more learning methods and contents that promote the normal development of their own minds and minds. In response to the problem that mind-normal education cannot be automatically integrated into the teaching of university thought and political science courses, in the context of artificial intelligence, this paper proposes a multi-channel-based mind-normal and ideological and political information fusion model. The model has two channels, BERT+CNN and BERT and BiLSTM-Attention; firstly, the pretraining model BERT is used to obtain the word vector representation of the fused text context; then, the CNN network of channel one is used to enhance the ability of local feature extraction of the text, and the BiLSTM-Attention model of channel two enhances the ability of long sequence text processing and is key. Finally, the fused features of channel 1 and channel 2 are classified using a softmax excitation function. To verify the effectiveness of the proposed model, experiments are conducted on public datasets to demonstrate the effectiveness of the proposed method.