Automated Recognition and Quantitative Categorization of Spatial Verbs in Zuo Zhuan: A Cognitive and Corpus-Based Study

In the context of accelerating the digital transformation of classical texts, this study explores the semantic system of spatial verbs in Zuo Zhuan through a cognitive linguistic lens and bioinformatic modeling.By leveraging the SikuBERT pre-trained language model, which is specifically fine-tuned for classical Chinese processing, this paper constructs a digital knowledge base of spatial verbs extracted from the Zuo Zhuan corpus.Through a multi-step framework involving automatic semantic annotation, part-of-speech tagging, similarity-based clustering, and vector encoding, the study identifies four core types of spatial verbs: motion, state, existence, and direction.Each verb type is further analyzed in relation to three quantitative dimensions: temporal-quantitative relations, behavioral-quantitative relations, and scene-component relations.The results reveal that motion verbs exhibit the highest semantic frequency and spatial-temporal diversity, while direction verbs are less frequent and predominantly metaphorical.Furthermore, the study introduces biomechanical concepts such as displacement, velocity, and dynamic trajectory to deepen the interpretative framework for motion verbs, thereby bridging linguistic representation with ancient depictions of human physical behavior.Evaluation metrics (Precision, Recall, and F1-score) indicate high accuracy in spatial verb classification using SikuBERT, confirming its effectiveness in ancient Chinese NLP tasks.