The　segmentation　of　bone　fragments　is　crucial　for　preoperative　planning　and　intraoperative　navigation　in　reduction　surgery.　Recent　advances　in　medical　segmentation　have　predominantly　focused　on　U-shaped　frameworks　that　employ　convolutional　neural　networks　or　transformer　variants　as　the　backbone.　However,　these　frameworks,　which　rely　on　a　single　encoder,　often　struggle　with　integrating　information　from　diverse　features　and　processing　irregular　shapes　in　visual　objects.　Such　limitations　can　reduce　segmentation　accuracy　and　impair　generalization　performance　across　different　datasets.　To　address　these　issues,　we　introduce　multi-information　fusion　network　based　on　dual-encoder　for　pelvic　bones　segmentation.　In　order　to　capture　global　contextual　information　and　local　features　simultaneously,　our　model　takes　alight　resnet　and　a　graph　neural　network　with　swin-pool　module　as　dual-encoder　for　effectively　representing　the　global　and　local　topologies.　We　construct　a　high-low　multi-dimensional　paired　attention　in　the　bottleneck　for　fusing　spatial　and　channel　information　from　different　dimensions.　Instead　of　using　the　traditional　dice　loss　in　the　unet-like　architecture,　our　model　employs　both　topological　loss　and　boundary　loss　to　enhance　the　goal　optimization.　In　the　experiments,　our　model　achieves　a　substantially　lower　dice　similarity　coefficient　and　comparable　95　Hausdorff　distance　compared　to　other　state-of-the-art.　The　experiments　on　across　datasets　verify　the　superiority　and　generalization　of　the　proposed　model.