Promoters are specific DNA sequences located in the upstream region of genes. By identifying and predicting promoters in DNA sequences, a better understanding of gene regulation mechanisms can be achieved, thereby advancing biological and medical research. Experimental methods for promoter prediction are both expensive and time-consuming. Additionally, computational methods for promoter prediction have limitations such as room for improvement in accuracy and insufficient information content in sequence encoding methods. This paper introduces a novel encoding approach that applies the pre-trained model DNABERT to promoter prediction. Different deep learning models are tested for prediction performance. Experimental results demonstrate that the Transformer model encoded using pre-trained and fine-tuned DNABERT achieves the best performance in promoter prediction tasks.