Research on the Innovative Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

To construct an automatic question-and-answer system for Civics, a neural network and a self-attention mechanism are combined in this paper. Through the input layer of the neural network, the model remembers the semantic information of the Civics text that has been processed. Dynamic feature extraction is used to categorize and classify Civics questions in the system and determine the type of questions. The GLU network is used to predict the answers to the Civics and Politics questions, and the predicted results are normalized by combining the Softmax function, and the category with the highest probability is selected as the answer. The main mode of teaching afterward was to combine the automatic question-and-answer system with teaching. The results show that in the question and answer system, the highest accuracy rate is the practical cognition type of questions, with the accuracy rate fluctuating in the range of (0.8-0.9), and the accuracy rate of truth type and method type of questions fluctuates up and down in the range of 0.7. The percentage of students in the high marking stage increased by 0.1. In Civic Behavior, students’ patriotism increased by 0.15, and spirituality increased by 0.3.