A deep learning model for innovative evaluation of ideological and political learning

College students’ creative ideological and political curriculum should combine current development in education, adhere to associated norms and rules, and adapt educational principles and procedures to ensure balanced success. IPE can produce significant results when using several methods to make ideological and political education material. Effective education must be evaluated with the most efficient and emerging technology. Therefore, this paper presents a deep learning model for teaching quality analysis (DLM-TQA). The proposed model implements a teaching error feedback mechanism associated with knowledge sources and audio and video source data from the classroom produced in the teaching process. The model produces reports of teaching irregularities by correlating subjective data with empirical data. The research leads to strengthening teacher instructional methods, learning approaches, and teacher management modes. It has valuable references for developing teacher education frameworks, creative teacher preparation models, and excellent teachers’ training plans. The experimental results based on student evaluation in political education are Students Political Evaluation Ratio is 87.66%, Students Virtual Learning Performance ratio is 88.77%, Innovative Thinking Percentage ratio is 84.5%, Reducing risk in political teaching Ratio is 82.26%, and Students efficiency ratio is 93.80%.