Abstract:

[Purpose/Significance] To address the issues of knowledge dispersion, redundancy, and fragmentation within the organization of complex historical events, this study employs the event evolution graph method. It explores their potential applications in the semantic organization of historical events and the structured representation of historical knowledge. The aim is to enhance the discoverability, reusability, and semantic relevance of historical data, while broadening the theoretical framework and practical approaches of the event evolution graph in historical research. [Method/Process] First, we constructed a three-layer framework for complex historical event, consisting of a data layer, a semantic layer, and an application layer. The data layer enables the collection of all event elements and supports multimodal integration, including text, images, and maps. The semantic layer implements AI-enhanced event representation and deep relationship mining. The application layer performs correlation calculation and multi-level graph visualization, allowing users to interactively explore event structures and semantic pathways. On this basis, three key tasks were carried out. First, AI-enhanced methods for representing and extracting complex historical events were designed. In particular, a dynamic ontology model for AI understanding was constructed based on the W7 model, formally represented as:e = {what, why, who, how, which, when, where, environment, effect, certainty}. This formalization systematically depicts event elements and their semantic relationships, capturing not only basic event components but also contextual factors, causal consequences, and the degree of historical certainty. Second, AI-enhanced methods for calculating the correlation of complex historical events were proposed. These methods combine rule-based reasoning with machine learning classifiers to identify and quantify semantic relationships such as causality, temporality, correlation, and hierarchy among events. A case study on a representative complex historical event was conducted to validate the proposed framework and methods. [Results/Conclusions] The study demonstrates that AI can improve the accuracy of extracting event elements and analyzing semantic relationships. This provides a feasible technical pathway for organizing historical knowledge computationally and providing intelligent services. The case study results show that the generated event evolution graph captures multi-level event structures and reveals previously implicit causal and evolutionary patterns. However, research in this field still faces challenges, including the scarcity of high-quality historical corpora, subjectivity when generalizing and decomposing events, and insufficient integration of multimodal information. In the future, we will focus on three directions: developing weakly supervised learning and transfer learning methods tailored to scenarios with sparse historical data; designing human-computer collaborative tools for event decomposition and relationship annotation to balance automation with scholarly interpretability; and constructing multimodal event evolution graph for complex historical events by incorporating visual, spatial, and audio data.

Key words: artificial intelligence, complex historical events, event evolution graph, 18th Army