Aimed at the problem of insufficient multi-scale feature information extraction and fusion in the remote sensing change detection task, which makes it difficult to accurately detect targets with significant shape differences, this paper proposes an MFFNet model for remote sensing image change detection. First, the model takes HRNet combined with Coord convolution as the backbone network, and a multi-scale difference feature extraction and fusion module is employed to improve the model's sensitivity to change information. Second, a dual time converter is constructed based on Transformer to model the remote sensing image context. Finally, CARAFE is used in the network prediction head to avoid the problem of small sensing field caused by up-sampling. The algorithm is experimentally validated on the remote sensing change detection dataset LEVIR-CD, WHC-CD, and DSIFN. The F1-scores are 90.75, 90.53, and 86.34 and intersection of unions are 83.07%, 82.70%, and 76.91%, which are higher than those of eight compar methods, which fully proves the validity of the algorithm in improving detection performance and accuracy.