“This paper addresses the task of identifying and classifying historical event trigger words in Classical Chinese, utilizing both small-scale and large-scale language models. Specifically, we selected the small-scale language model GujiBERT for intelligent processing of classical texts, and the large-scale language model Xunzi-Qwen-14b. Both models underwent continued pretraining and fine-tuning, resulting in GujiBERT-CHED-mlm and Xunzi-Qwen-14b-CHED. For the small-scale language model, we used a BiLSTM as the feature extraction module and a CRF as the decoding module, employing a sequence labeling paradigm to complete the evaluation experiments. For the large-scale language model, we optimized the prompt templates and used a sequence-to-sequence paradigm for evaluation experiments. Our experiments revealed that GujiBERT-BiLSTM-CRF achieved the best performance across all tasks, ranking fourth in overall performance among all participating teams. The large-scale language model demonstrated good semantic understanding abilities, reaching a preliminary usable level. Future research should focus on enhancing its ability to produce standardized outputs.”