Research on an Evaluation Framework for Cross-Media Generation Algorithms and Reinforcement Learning Teaching Methods for Ancient Chinese Literature

The rapid development of intelligent information systems has created increasing demand for robust cross-modal representation, multimodal signal processing, and adaptive learning mechanisms, which are also of practical significance for electromagnetic information transmission and human–machine interaction in next-generation communication environments. This study proposes an integrated evaluation framework that combines cross-media generation algorithms with reinforcement learning-based teaching strategies for ancient Chinese literature. The framework incorporates a largescale cultural knowledge graph, dual-modality feature encoding, dynamic cultural constraint injection, and multimodal learning analysis to improve semantic consistency and educational effectiveness. Experimental results demonstrate that the proposed method increases the cultural retention rate of generated content to 93.2%, achieves a cognitive load warning accuracy of 92.7%, and significantly enhances learners’ cultural understanding while supporting large-scale concurrent educational applications with reduced computational cost. By coupling cross-modal generation and adaptive optimization, the framework provides a quantitative methodology for digital cultural heritage preservation and intelligent educational systems. Moreover, its multimodal representation and evaluation mechanisms offer valuable references for information fusion, signal-aware content generation, and cross-domain knowledge transmission in electromagnetic and communication-oriented intelligent systems.