This study investigates author attribution (AA) in Japanese texts through fine-tuning the pre-trained BERT model “cl-tohoku/bert-large-japanese-v2” with Japanese-specific stylometric features. Experiments explored combinations of these features with classifiers such as LR, SVM, and RF, across varying author counts from 5 to 75. Focusing solely on native Japanese compositions, the study utilized the “Composition Bilingual Database” from the National Institute for Japanese Language and Linguistics to maintain linguistic consistency. The BERT model combined with LR achieved the highest accuracy of 96.3% for 5 authors, demonstrating deep learning’s potential in Japanese AA. However, high-dimensional stylistic features introduced noise when integrated, highlighting challenges in feature alignment. Future work will explore advanced non-linear models like XGBoost, LightGBM, and CatBoost for improved feature integration, and low-resource classification methods such as prototypical networks to enhance performance without extensive dataset expansion. Additionally, further testing of alternative Japanese pre-trained language models will be conducted to capture linguistic nuances more effectively.