Design optimization of university ideological and political education system based on deep learning

This study seeks to enhance the effectiveness and student engagement in university ideological and political education (IPE) by leveraging deep learning technology. Traditional IPE approaches often fall short in terms of flexibility and interactivity, resulting in diminished student participation. The advancement of deep learning technology offers new opportunities for IPE due to its powerful capabilities in feature extraction and pattern recognition. This research employs a CNN-LSTM hybrid model, integrating Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM). By analyzing students' learning needs and interests, personalized learning paths and resource recommendations are provided for them. Firstly, this paper introduces the research methods in detail, including deep learning algorithm and model design, as well as the optimization design process of IPE system. The hybrid model combines the advantages of CNN in feature extraction and the ability of LSTM in processing sequence data to realize accurate analysis of IPE data. In the discussion part of experiment and result analysis, the model is trained and verified by collecting multi-channel data related to university IPE and using high-performance server. The results show that CNN-LSTM hybrid model is superior to traditional methods such as SVM and random forest in accuracy, recall and F1 score. This proves the powerful ability of deep learning model in dealing with complex data and capturing the internal laws and relationships of data. This study optimizes the design of university IPE system through deep learning technology, which not only improves the pertinence and effectiveness of education, but also provides new ideas and directions for the application of deep learning in the field of education.