To tackle the segmentation challenges arising from the complex morphology, significant size variations, blurred boundaries, and tight interconnections of superalloy grains, this research presents a grain segmentation network integrated with a joint suppression attention mechanism that fuses channel and spatial information. The proposed network combines the global modeling capacity of Swin Transformer and the local detail restoration capability of a convolutional neural network (CNN), and embeds the aforementioned joint suppression attention mechanism, which integrates channel and spatial information, into the decoder. Results show that this mechanism effectively suppresses noise and texture interference, enhances the abilities of feature screening and generalization, and reinforces the fusion of shallow and deep features, thereby markedly improving the continuity of grain boundaries. Experimental results demonstrate that the proposed algorithm achieves an IoU of 67.34% and an F1-score of 78.62% on the self-constructed metallographic dataset, with all metrics outperforming those of mainstream grain segmentation algorithms for superalloys.