[Purpose/Significance] There are a large number of ancient classical books in China. Automatic sentence segmentation and punctuation of ancient book texts using computers is helpful to speed up the transformation and utilization of ancient books. There are two urgent problems in the existing research which need to be solved. First, the previous research divides automatic sentence segmentation and punctuation of ancient books into two serial tasks, which causes error accumulation. Second, the punctuations automatically tagged are relatively chaotic, and there is less research on tagging long-distance nested pairwise quotation marks. [Method/Process] Based on the statistics of punctuation frequency in a large-scale ancient books corpus and the punctuation usage standards, the paper clarified the punctuation system used in automatic punctuation of ancient books. As the sentence segmentation can be inferred by the stop punctuations, an integrated solution of sentence segmentation and punctuation was proposed, and automatic punctuation was directly carried out on ancient texts without sentence segmentation. By designing a multiple-tag set and filling placeholders at the beginning of paragraphs, the problem of automatic tagging of long-distance nested pairwise quotation marks was solved. Within the framework of sequence labeling, the algorithm used SikuRoBERTa-BiLSTM-CRF to train model on the corpus of traditional Chinese ancient books which contains more than 100 million characters. [Result/Conclusion] In the open test Zuo Zhuan, the F1 score of stop punctuations tagging is 77.09%, sentence segmentation is 91.72%. The F1 score of a single quotation marks tagging is 89.28%, and the pairwise quotation marks tagging is 83.88%. The results show that the method in the paper effectively improves the effect of automatic sentence segmentation and punctuation of ancient books, and effectively solves the problem of automatically tagging quotation marks.