One of the important topics in the research field of Chinese classical poetry
is to analyze the poetic style. By examining the relevant works of previous
dynasties, researchers judge a poetic style mostly by their subjective
feelings, and refer to the previous evaluations that have become a certain
conclusion. Although this judgment method is often effective, there may be some
errors. This paper builds the most perfect data set of Chinese classical poetry
at present, trains a BART-poem pre -trained model on this data set, and puts
forward a generally applicable poetry style judgment method based on this
BART-poem model, innovatively introduces in-depth learning into the field of
computational stylistics, and provides a new research method for the study of
classical poetry. This paper attempts to use this method to solve the problem
of poetry style identification in the Tang and Song Dynasties, and takes the
poetry schools that are considered to have a relatively clear and consistent
poetic style, such as the Hongzheng Qizi and Jiajing Qizi, Jiangxi poetic
school and Tongguang poetic school, as the research object, and takes the poems
of their representative poets for testing. Experiments show that the judgment
results of the tested poetry work made by the model are basically consistent
with the conclusions given by critics of previous dynasties, verify some
avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry
style recognition in the Tang and Song dynasties.

本文针对中国古典诗歌研究领域中诗歌风格的分析进行了探讨，通过构建最完善的中文古诗数据集，训练基于 BART-poem 预训练模型的通用诗歌风格判断方法，引入深度学习到计算风格研究中，提供了一种新的研究方法，试图用这种方法解决唐宋时期的诗歌风格识别问题。实验证明，该模型对于代表诗歌流派的诗人的诗歌进行的判定结果与先贤的结论基本一致，更好地解决了唐宋时期诗歌风格辨识的任务。