The fundamental focus of national spiritual civilization creation has been the effects of college ideological and political instruction. The contemporary teaching methods exhibit greater flexibility, leading to a lack of reasonable assessment of ideological and political education quality. To tackle this issue, we suggest implementing a technique that utilizes a recurrent neural network (RNN) to assess the standard of IPE. Additionally, we want to develop an automated assessment system specifically designed for this purpose. We gathered a dataset (Student satisfaction, course size, classroom feedback, teacher efficacy and course research). The study employs Min-Max normalization to eliminate redundant elements and ensure uniformity and principal component analysis (PCA) is used to discover relevant properties using already processed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. A simulation environment was constructed to test the proposed approach, yielding notable performance metrics, Accuracy (95.68%), Precision (94.52%), Recall (86.59%) and F1-Score (88.56%). Comparative analysis demonstrates the efficacy of the suggested strategy, addressing limitations related to data availability and network complexities. Future efforts seek to improve RNN structures for various instructional materials, increase the clarity of assessments for better understanding and utilize large statistics to strengthen the model's resilience, resulting in a comprehensive manner supported by evidence based acceptance of the impact of IPE.