In order to address the issues of missing and leaking instance masks caused by redundant semantic information in mainstream single-stage instance segmentation algorithms, this paper proposes an instance segmentation algorithm based on semantic alignment and graph node interaction. In the global mask generation stage, a semantic alignment module was designed to evaluate the influence of semantic information on global and local semantic integrity through global mapping and Gaussian mapping, thereby suppressing redundant semantic information. In addition, a graph node interaction module was designed in the instance mask assembly stage that extracts spatial features of the topological graph by transforming the feature map into graph-structured data and interacting with graph node information, supplementing the mask assembly information and further improving the accuracy of the instance masks. The experimental results demonstrate that the proposed algorithm achieves a mean average accuracy (mAP) of 38.3% on the MS COCO dataset, exhibiting strong competitiveness against other state-of-the-art algorithms.