Constructing a knowledge graph for accidental explosion damage using investigation reports of explosion accident characterized by multi-source, heterogeneous, and overlapping information plays a significant role in data-driven explosion assessment and traceability analysis. To address the overlapping and nested events in accidental explosion investigation data, a knowledge graph construction method centered on event joint extraction was employed, utilizing explosion investigation reports to build the accidental explosion damage knowledge graph. By retrieving similar explosion events within the knowledge graph using cosine similarity and applying a Bayesian classification method, the type of explosive materials involved in the Beirut port explosion incident was identified with relatively high accuracy. The knowledge graph construction results demonstrate that on the accidental explosion damage corpus, the proposed dynamic masking-based event joint extraction method improved the F₁ scores for event classification and event element classification by at least 2% and 5.4%, respectively, compared to existing extraction models. Traceability analysis indicates that knowledge graph-based traceability offers significant improvements in both speed and accuracy compared to traditional manual traceability methods.