Change detection in remote sensing images is crucial for various applications such as military reconnaissance and urban management. However, traditional change detection methods suffer from low accuracy and complex operations. Meanwhile, existing deep learning approaches struggle to fully understand multi-scale semantic information, and thus still face limitations in accuracy and generalization capability. To overcome these limitations, this paper proposed the MSTAN, which consists of a multi-scale Transformer encoder and a decoder centered on a four-layer ASFF module. The four-layer ASFF module dynamically learns spatially adaptive weights to capture multi-scale semantic information. Comparative experiments demonstrate that MSTAN achieves high-precision change detection. Cross-dataset evaluation experiments demonstrate that MSTAN possesses strong generalization ability. Ablation experiments confirm the effectiveness of the four-layer ASFF module in fusing multi-scale features. Complexity analysis quantifies the computational overhead of the four-layer ASFF module. These results highlight MSTAN’s powerful generalization capability and its promising potential for change detection tasks.