[Purpose/Significance] The presentation and mining of multidimensional data can provide richer knowledge services for the scene reconstruction and knowledge discovery of historical events. Traditional network analysis in existing research is limited by the constraints of a single data dimension, so there is an urgent need for network analysis methods that can support multidimensional data and multiple types of correlations to enhance historical event scene reconstruction and knowledge discovery. [Method/Process] This article proposes a model for historical event scene reconstruction and knowledge discovery based on PLM-NERE-MMNs, which improves classic multilayer networks with AI empowerment. Based on the proposed model, an empirical study is conducted on the Yiwa Campaign as a case study. [Result/Conclusion] The study results indicate that the improved multilayer network can comprehensively and effectively represent multidimensional data from text resources, enabling a holographic presentation of historical events. By analyzing the state evolution of multilayer networks and relevant network indicators, implicit knowledge behind historical events can be revealed. The PLM-NERE-MMNs-based model for scene reconstruction and knowledge discovery proposed in this article provides a new method and perspective for knowledge discovery in the field of digital humanities.
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