[Purpose/Significance] Narrative texts of cultural relics are important cultural heritage data resources in China. Using AIGC technology to mine and organize the semantic features of events within these texts is of great significance for the protection and development of the digital memory of cultural heritage. [Method/Process] This paper proposes a semantic organization method for cultural heritage narrative texts based on event ontology, driven by large language model(LLM) reinforcement learning. Firstly, event concepts are revealed and knowledge units are reorganized by integrating expert wisdom with text features, and the core metadata in the relic narrative texts are summarized to form a relic event ontology. Secondly, the DeepSeek reasoning paradigm is utilized for the cold-start of event extraction to obtain pseudo-labels, and human-AI collaboration is introduced to optimize the quality of the corpus. Finally, this paper focuses on designing efficient and adaptive reward functions for the task framework of event extraction, and explores the effectiveness of LLM reinforcement learning for event mention extraction, event classification, and event argument recognition through group relative advantage computation. [Result/Conclusion] The DeepSeek-R1-Distill-Qwen-32B model, optimized with group relative policy optimization, achieves the best performance in the event mention extraction(BLEU＿1=80.21%, ROUGE-1-F1=84.64%) and event argument recognition(F1=78.90%) tasks, respectively. The reinforced QwQ-32B demonstrates superior performance in the event classification task(ACC=86.09%). Entity disambiguation and coreference resolution can further optimize the quality of the event knowledge graph, enabling digital narrative services for cultural relics in terms of historical plot analysis, character knowledge genealogy, and spatiotemporal coupling analysis.
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