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| Deep Learning-Driven Multilabel Classification and Semantic Relationship Mapping of Disaster Entities in Chinese Meteorological Historical Materials |
| Hu Zewen, Xie Shaoke |
| School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044 |
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Abstract This study aimed to address the automatic identification, multilabel classification, knowledge graph construction, and cyclical dynamic evolution of disaster entities and their semantic relationships in Chinese meteorological historical materials. Meteorological historical data from the early Ming Dynasty, drawn from the Chinese Three Thousand Years of Meteorological Records Collection, were used as the study sample. The BERT-RCNN (bidirectional encoder representations from transformers - recurrent convolutional neural networks) deep learning model was applied to extract and classify disaster entities and to perform multilabel classification of disaster records in the historical data. Based on this process, structured data were generated and used to construct the schema layer of semantic relational mapping of historical meteorological hazard entities. The integrated use of the Neo4j graph database and Gephi visualization tools for map visualization and cyclical evolution analysis revealed the temporal changes and spatial distribution characteristics of meteorological hazards in the early Ming Dynasty. The results showed that the BERT-RCNN model demonstrated significant performance advantages in the automatic identification and multilabel classification of meteorological disaster entities in Chinese meteorological historical data, achieving a mean micro-F1 classification precision and disaster record recall of up to 96%. Differences in recognition and multilabel classification performance were observed among different disaster entities. The model exhibited optimal recognition and classification performance for disaster entities such as floods and droughts, which occurred more frequently in the early Ming Dynasty, while recognition and identification performance was poorer for a small number of disaster types with lower occurrence frequencies. The semantic relationship mapping of historical meteorological disaster entities, constructed on the basis of structured data obtained after multilabel classification, clearly revealed the temporal and spatial variation characteristics of different types of disaster entities in Chinese meteorological historical data. From a temporal perspective, the frequency of meteorological disasters in the early Ming Dynasty in China exhibited an overall trend of a slight decrease, followed by a gradual increase, and then a sharp increase in the final stage. The main disaster types affecting societal development in the early Ming Dynasty in China were floods, droughts, and locusts. With respect to spatial distribution, droughts and floods exhibited higher frequencies and greater spatial concentration in the middle and lower reaches of the Yangtze River and the lower reaches of the Yellow River.
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Received: 31 May 2025
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