Change detection is an important branch in remote sensing image processing. Deep learning has been widely used in this field. In particular, a wide variety of attention mechanisms have made great achievements. However, some models have become increasingly complex and large, often unfeasible for edge applications. This poses a major obstacle to industrial applications. In this paper, to solve the above challenges, we propose a Lightweight network structure to improve results while taking into account efficiency. Specifically, first, the shallow features are extracted by using the spatial exchange and change exchange of the down-sampling bi-temporal channel of the three-layer EfficientNet backbone network, and then the shallow features are used for low-dimensional skip-connection. After that, a hybrid dual-temporal data module is designed to mix the dual-temporal phase into a single image, then the high-dimensional low-pixel image is restored through the up-sampling. Finally the final change map is generated through the pixel-level classifier. Our method was evaluated on public datasets by evaluation indicators such as OA, IoU, F1, Recall, Precision.