Aiming at the problem that it is difficult to recognize object grasp points because of overlap or occlusion between multiple objects in stacked scenes, a multiscale regional-attention stacked-object grasp detection network is proposed. First, a multiscale regional-attention feature fusion module is proposed based on the feature pyramid architecture, which improves the network's ability to pay attention to different feature dimensions by introducing deformable convolution and full convolution. Second, a multiscale region-attention mechanism is used to decouple the grabbable area from the background in the stacked scene image. Different regions of different scale feature maps are weighted gradually to improve the network's ability to pay attention to the saliency of the grabbable area and its background-noise anti-interference ability. Finally, a double sampling region candidate module is proposed to further refine the candidate anchor boxes on the basis of the target ground truth, eliminate a large number of negative samples, and thus improve the quality of the candidate anchor boxes. The final grasp detection results are output by the classification regression module. Stacked-object grasp detection accuracy experiments are carried out on the VMRD and Cornell datasets. The experimental results show that the average detection accuracy of the proposed network on the VMRD dataset is 98.18%, whereas it is 98.0% on the Cornell dataset. The proposed network has accurate grasp detection effect and strong robustness in complex scenes.