Spatial　transcriptomics　(ST　technology　allows　for　the　detection　of　cellular　transcriptome　information　while　preserving　the　spatial　location　of　cells.　This　capability　enables　researchers　to　better　understand　the　cellular　heterogeneity,　spatial　organization,　and　functional　interactions　in　complex　biological　systems.　However,　current　technological　methods　are　limited　by　low　resolution,　which　reduces　the　accuracy　of　gene　expression　levels.　Here,　we　propose　scstGCN,　a　multimodal　information　fusion　method　based　on　Vision　Transformer　and　Graph　Convolutional　Network　that　integrates　histological　images,　spot-based　ST　data　and　spatial　location　information　to　infer　super-resolution　gene　expression　profiles　at　single-cell　level.　We　evaluated　the　accuracy　of　the　super-resolution　gene　expression　profiles　generated　on　diverse　tissue　ST　datasets　with　disease　and　healthy　by　scstGCN　along　with　their　performance　in　identifying　spatial　patterns,　conducting　functional　enrichment　analysis,　and　tissue　annotation.　The　results　show　that　scstGCN　can　predict　super-resolution　gene　expression　accurately　and　aid　researchers　in　discovering　biologically　meaningful　differentially　expressed　genes　and　pathways.　Additionally,　scstGCN　can　segment　and　annotate　tissues　at　a　finer　granularity,　with　results　demonstrating　strong　consistency　with　coarse　manual　annotations.　Our　source　code　and　all　used　datasets　are　available　at　https://github.com/wenwenmin/scstGCN　and　https://zenodo.org/records/12800375.