Remote sensing change detection refers to the process of identifying and extracting changes in objects within the same geographical region over multiple periods. With the increasing spatial resolution of remote sensing images, the detection of minor changes has become a challenging task. We introduce a multilevel feature aggregation and enhancement network to tackle this issue. Specifically, we propose a multilevel feature aggregation module to aggregate the distinct features extracted from each image, which strengthens the feature representation capability. Subsequently, a difference parallel mapping module is designed to perceive information at different scales by refining the fused features. In addition, our guided change enhancement module captures local and long-range dependencies in multilevel features, improving the network’s accuracy in identifying changing regions. Based on a basic shared weight Siamese backbone without complex structures, our model outperforms other state-of-the-art methods on three datasets in terms of both efficiency and effectiveness.