Convolutional Neural Network based Framework for Intelligent Evaluation of Ideological and Political Education

Assessing Ideological and Political Education (IPE) has become a key focus in modern educational reform, especially within the context of intelligent learning environments. The conventional approaches such as manual comments, fixed indicators, and subjective impressions are not adaptable, objective, and in real-time. This study addresses existing gaps by proposing a deep learning evaluation framework that integrates a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The model extracts both spatial and temporal characteristics from multimodal student data, such as engagement measures, sentiment scores, classroom behaviour, and feedback logs. The CNN-LSTM model differs from traditional rule-based or expert-scoring systems since it allows for automated, objective, and dynamic assessment. CNN effectively learns spatial features, whereas LSTM captures sequential dependencies; thus, the hybrid model is ideally suited to analyse intricate IPE datasets. Experimental results indicate the model has a 94.7% accuracy and a 93.8% F1-score, performing better than conventional machine learning methods. The system also features a real-time feedback analysis and performance prediction module, further improving assessment accuracy. This framework represents a significant leap in intelligent, data-driven IPE evaluation systems.