Background:　Mass　Spectrometry　Imaging　(MSI)　is　a　label-free　imaging　technique　used　in　spatial　metabolomics　to　explore　the　distribution　of　various　metabolites　within　biological　tissues.　Spatial　segmentation　plays　a　crucial　role　in　the　biochemical　interpretation　of　MSI　data,　yet　the　inherent　complexity　of　the　data-characterized　by　large　size,　high　dimensionality,　and　spectral　nonlinearity-poses　significant　analytical　challenges　in　MSI　segmentation.　Although　deep　learning　approaches　based　on　convolutional　neural　networks　(CNNs)　have　shown　considerable　success　in　spatial　segmentation　for　biomedical　imaging,　they　often　struggle　to　capture　the　comprehensive　structural　information　of　MSI　data.　Results:　We　propose　SagMSI,　an　unsupervised　graph　convolution　network　(GCN)-based　segmentation　strategy　that　combines　spatial-aware　graph　construction　of　MSI　data　with　a　GCN　module　within　a　deep　neural　network.　This　approach　enables　flexible,　effective,　and　precise　spatial　segmentation.　We　applied　SagMSI　to　both　simulated　data　and　various　MSI　experimental　datasets　and　compared　its　performance　against　three　commonly　used　segmentation　methods,　including　t-SNE　+k-means,　a　pipeline　implemented　by　the　R　package　Cardinal,　and　a　CNN-based　segmentation　method.　Visual　comparisons　with　histological　images　and　quantitative　evaluations　using　the　silhouette　coefficient　and　adjusted　rand　index　demonstrate　that　SagMSI　excels　in　segmenting　complex　tissues,　revealing　detailed　sub-structures,　and　delineating　distinct　boundaries　of　sub-organs　with　minimal　noise　interference.　The　integration　of　graph-based　neural　networks　with　spatially　structural　information　offers　deeper　insights　into　spatial　omics.　Significance:　The　MSI　data　is　modelled　by　graph　structure　so　as　to　incorporate　the　biomolecular　profiling　and　spatial　adjacency　within　neighboring　pixels.　The　GCN　framework　generates　meaningful　pixel　representations　by　learning　local　and　global　contextual　information　through　the　graph-based　structure,　thus　enabling　precise　segmentation　of　MSI.　The　approach　demonstrated　high　flexibility,　robustness　to　noise,　and　applicability　in　exploring　complex　tissue　structures　and　identifying　marker　ions　associated　with　microregions.