A majority of research methods neglect significant variations exhibited in the style of bi-temporal remote sensing images acquired at different times for the same area, leading to unsatisfactory model performance indexes and visualization when the model is applied to stylistically diverse datasets. To address this issue, a style transfer module is used in this article to generate an image with a style similar to that of another moment for the original image at a certain moment. Subsequently, a symmetrical difference feature pyramid network (SDFPNet) based on bi-directional style transfer is proposed to determine the influence degree of different style transfer directions on the improvement of change detection accuracy. Specifically, two lightweight Siamese networks and difference feature pyramid network (DFPNet) are used to conduct parameter optimization on the inputted original and stylized as SDFPNet, producing the change maps predicted by two parallel branches. To reduce the misclassification of changed pixels, the two prediction results are merged to improve the accuracy of change detection. Experiments on three datasets, LEVIR-CD, CDD, and SYSU-CD, demonstrate that the proposed SDFPNet based on bi-directional style transfer outperforms state-of-the-art (SOTA) methods in remote sensing change detection task, with results of CDD and SYSU-CD datasets more convincing, which have large style differences due to seasonal changes. The detection accuracy reaches 99.37% and F2 score reaches 94.19% on the CDD dataset, and the detection accuracy reaches 92.31% on the SYSU-CD dataset. The proposed method in this article effectively solves the problem of poor change detection accuracy caused by large style differences in bi-temporal images.