Currently, popular neural networks not only struggle to accurately recognize various types of surface targets but also tend to introduce significant noise and errors when handling limited samples and weak supervision. Therefore, this study proposes a dual-network remote sensing image classification method based on dynamic weight deformation, after analyzing the features of remote sensing images. By constructing a flexible, simple, and effective weight dynamic deformation structure, we establish an improved classification network and target recognition network. This introduces the self-verification ability of dual network comparison, thereby enhancing learning performance, error correction, recognition efficiency, supplementing omissions, and improving classification accuracy. Experimental comparisons show that the proposed method is easy to implement and exhibits stronger cognitive ability and noise resistance. It confirms the adaptability of the proposed method to various remote sensing image classification tasks and its vast application potential.