Aiming at the problems of low accuracy and efficiency of existing mainstream registration models due to the high resolution and large variation of target scale in remote sensing images, this paper proposes a registration model based on dynamic mix attention and multi-scale features. First, the ConvNeXt V2 network is introduced in the feature extraction stage, which enhances the ability to dynamically link the global context and local details by embedding dynamic mix attention. Next, a multi-scale feature calibration module is proposed to aggregate low-level texture information with high-level semantic information in multiple receptive fields to improve the registration accuracy. Then, a bi-directional matching method based on prediction matrix weighting is used in the matching stage to compute the dense correspondence to obtain the bi-directional parameters, and finally the image registration is completed by affine transformation. Experimental results on three datasets such as Aerial Image show that, the registration accuracy reaches 0.409, 0.864, and 0.953 with normalized distance thresholds of 0.01, 0.03, and 0.05, and the average registration time is 0.87 s. The results prove that the model effectively improves the accuracy and efficiency of remote sensing image registration.