Based on integrate deep learning and machine learning, an annotated dataset oriented towards authorship verification is constructed, employing BERT and dynamic similarity threshold strategy to enhance label quality. An authorship identification model integrating BERT text embedding and XGBoost-BO is proposed. This model combines the powerful feature extraction capabilities of BERT and the efficient classification performance of XGBoost. Additionally, Bayesian optimization is employed for hyperparameter search to achieve accurate authorship verification. Furthermore, the effectiveness of the dynamic similarity threshold strategy in enhancing author similarity determination accuracy is investigated, as well as the significant role of Bayesian optimization in automatically adjusting XGBoost hyperparameters to improve its overall performance. Experimental results demonstrate that the proposed method outperforms all baseline methods across various evaluation metrics, providing an effective solution for authorship verification. 

基于深度学习与机器学习相结合的方法, 构建面向作者身份验证的标注数据集, 并采用BERT和动态相似度阈值策略来提升标签质量。然后, 提出一种融合BERT文本嵌入和XGBoost-BO的作者身份识别模型, 该模型通过结合BERT强大的特征提取能力、XGBoost高效的分类性能以及贝叶斯优化的超参数搜索策略, 实现对作者身份的准确判断。同时, 探讨动态相似度阈值策略在提升作者相似度判定准确性方面的有效性, 以及贝叶斯优化在自动调整XGBoost超参数、提升模型综合性能方面的显著作用。实验结果表明, 该方法在各项指标上均优于其他对比算法, 可为作者身份验证提供新的思路和方法。