Multimodal Representation Learning and Agentic Entity Generation Model for Cross-Cultural Characteristics of Anglo-American Literature

This study addresses the challenges of cultural symbol attenuation, character attribute drift, and scenesemantic misalignment in cross-cultural entity generation for Anglo-American literature. To this end, the Anglo-American Literary Intellectual Property Cross-Cultural Feature Agent-Based Entity Generation Model is proposed, integrating multimodal representation learning with an agent-based closed-loop verification mechanism. The study constructs character, scene, and cultural symbol features based on the LitBank dataset, and further employs the Georgetown University Multilayer Corpus to conduct cross-domain robustness evaluation. The model integrates Generative Adversarial Network-based generation, Convolutional Neural Network-based multi-scale feature enhancement, and Bidirectional Recurrent Neural Network-based semantic correction within a unified optimization framework. Cultural weighting, semantic deviation, and entity consistency are jointly optimized during training. Experimental results indicate that cultural feature matching improves by 19.7%, entity attribute consistency improves by 22.3%, cross-cultural semantic deviation decreases by 14.7%, and character entity feature matching improves by 11.8%. Across five repeated trials, all standard deviations remain below 2.1. The findings suggest that agent-based constraints combined with multimodal representation learning enhance the stability and interpretability of cross-cultural literary entity generation, providing a methodological reference for literary digitization and computational modeling of cultural symbols.