[目的/意义] 典籍是我国传统文化、思想和智慧的载体，结合数字人文的数据获取、标注和分析方法对典籍进行实体自动识别，对于后续应用研究具有重要意义。[方法/过程] 基于经过自动分词与人工标注的25本先秦典籍构建古籍语料库，分别基于不同规模的语料库和Bi-LSTM、Bi-LSTM-Attention、Bi-LSTM-CRF、Bi-LSTM-CRF-Attention、Bi-RNN和Bi-RNN-CRF、BERT等7种深度学习模型，从中抽取构成历史事件的相应实体并进行效果对比。[结果/结论] 在全部语料上训练得到的Bi-LSTM-Attention与Bi-RNN-CRF模型的准确率分别达到89.79%和89.33%，证实了深度学习应用于大规模文本数据集的可行性。
[Purpose/significance] The classics are the carrier of Chinese traditional culture, thought and wisdom. Combining the methods of data acquisition, labeling and analysis of digital humanities, it is of great significance for the automatic entity recognition of classics for subsequent application research. [Method/process] The corpus was constructed based on 25 pre-Qin literature that have been automatically segmented and manually annotated, based on the corpus of different sizes and seven deep learning models of Bi-LSTM, Bi-LSTM-Attention, Bi-LSTM-CRF, Bi-LSTM-CRF-Attention, Bi-RNN, Bi-RNN-CRF and BERT, we extracted the corresponding entities that constituted historical events and compared their effects.[Result/conclusion] The accuracy of the Bi-LSTM-Attention and Bi-RNN-CRF models trained on all corpus reached 89.79% and 89.33%, respectively, confirming the feasibility of applying deep learning to large-scale text datasets.
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