This thesis proposes a spam detection method based on BERT (Bidirectional Encoder Representations from Transformers) model. Spam problem is becoming more and more serious in today's Internet environment

and effective identification and filtering of spam is crucial to protect users' information security and improve the quality of mail services.This study describes in detail the implementation steps of the BERT model-based spam detection method. First

we use a pre-trained BERT model as a feature extractor to convert email text data into a BERT representation. Then

fine-tuning (fine-tuning) is performed using the labeled spam dataset to learn decision bounds for mapping the BERT representation to spam classes by training a classifier. Finally

we evaluate the performance of the method using a test dataset and compare it with other commonly used spam detection methods.Experimental results show that the spam detection method based on the BERT model achieves significant improvements in metrics such as accuracy and recall. Compared with traditional rule-based or feature engineering methods

this method can better capture the complex semantic and contextual information in spam

thus improving the accuracy of spam detection.The spam detection method based on BERT model proposed in this study has good performance and application prospects. Future research can further explore how to combine other technical means and optimization strategies to further improve the effectiveness of spam detection and promote the wide application and diffusion of the method in practical applications.