Abstract: 【Purpose/significance】The aim of this paper is to study a small number of annotated training samples to construct a sen‐tence segmentation model of ancient texts, reduce the cost of the sample annotation in the process of model training, and provide a new idea for exploring the integration of digital technology and humanities【. Method/process】Based on the uncertainty and diversity of an‐cient text samples, this paper proposes a weighted multi-strategy sample selection method to train the sentence segmentation model for ancient texts, which effectively combines BERT-BiLSTM-CRF, and BERT-CRF models. Then, based on the concepts of information entropy and similarity, the uncertainty and diversity of ancient texts are analyzed in depth, and the value of ancient text samples for models training is evaluated by weighted quantitative calculation. the weighted multi-strategy sample selection method is applied to
the training of sentence segmentation models of ancient texts, which selects valuable samples and updates them to the model training set after labeling, the model will then be trained iteratively.【Result/conclusion】The ancient book History of Song Dynasty is taken as an example, the proposed method can reduce the original training sample size by 50% and 55% respectively in the training process of ancient text segmentation models such as BERT-BiLSTM-CRF and BERT-CRF, which further verifies the effectiveness of the pro‐posed method.【Innovation/limitation】The weighted multi-strategy sample selection method provides a new idea for the training of an‐cient text segmentation models. Especially, it will explore the applicability of the proposed method in other tasks of ancient texts colla‐tion in the future.