A remote sensing rotating target detection approach based on a sparse Transformer is proposed to address the problem of remote sensing image target detection, which is challenging due to the wide neighborhood sparse, multi-neighborhood aggregation, and multiple orientations characteristics. First, this method uses the K-means clustering algorithm to produce multi-domain aggregation, to better extract the target features in the sparse domain, based on the typical end-to-end Transformer network, and the characteristics of a remote sensing image. Second, to adapt to the basic characteristics of the rotating target, a learning technique based on the target bounding box’s center point and the frame features is proposed in the frame generation stage, to efficiently obtain the target regression oblique frame. Finally, the network’s loss function is further optimized to improve the detection rate of the remote sensing target. The experimental results on DOTA and UCAS-AOD remote sensing datasets show that the average accuracy of this technique is 72.87% and 90.4%, respectively; thus indicating that it can adapt effectively to the shape and distribution characteristics of various rotating targets in remote sensing images.