In　recent　years,　with　the　continuous　advancement　of　remote　sensing　(RS)　technology　and　text　processing　techniques,　there　has　been　a　growing　abundance　of　RS　images　and　associated　textual　data.　Combining　RS　images　with　their　corresponding　textual　data　allows　for　integrated　analysis　and　retrieval,　which　holds　significant　practical　implications　across　multiple　application　domains,　including　geographic　information　systems　(GIS),　environmental　monitoring,　and　agricultural　management.　RS　images　have　the　characteristics　of　multitargets　and　multiscales,　and　the　textual　descriptions　of　these　targets　are　not　fully　utilized,　leading　to　a　decrease　in　retrieval　accuracy.　Previous　methods　have　struggled　to　balance　intermodality　information　interaction　and　intramodality　feature　fusion,　and　they　have　paid　little　attention　to　the　consistency　of　distribution　within　modalities.　In　light　of　this,　this　article　proposes　a　symmetric　multilevel　guidance　network　(SMLGN)　for　cross-modal　retrieval　in　RS.　SMLGN　first　introduces　fusion　guidance　between　local　and　global　within　modalities　and　fine-grained　bidirectional　guidance　between　modalities,　allowing　for　the　learning　of　a　common　semantic　space.　Furthermore,　to　address　the　distribution　differences　of　different　modalities　within　the　common　semantic　space,　we　design　an　adversarial　joint　learning　framework　and　a　multiobjective　loss　function　to　optimize　the　SMLGN　method　and　achieve　consistency　in　data　distribution.　The　experimental　results　demonstrate　that　the　SMLGN　method　performs　well　in　the　task　of　cross-modal　retrieval　between　RS　images　and　textual　data.　It　effectively　integrates　the　information　from　both　modalities,　improving　the　accuracy　and　reliability　of　the　retrieval　process.