Remote　sensing　image–text　retrieval　aims　to　establish　semantic　alignment　between　images　and　texts　to　enable　accurate　cross-modal　retrieval.　Existing　methods　usually　extract　features　from　images　and　texts　independently,　aligning　them　in　a　shared　embedding　space　to　achieve　cross-modal　retrieval.　However,　these　methods　often　assume　complete　alignment　between　image–text　pairs,　overlooking　the　inherent　disparities　between　the　rich　visual　details　in　remote　sensing　images　and　the　abstract　nature　of　textual　descriptions.　These　disparities　result　in　image–text　pairs　only　sharing　partial　semantic　correlations,　rather　than　one-to-one　complete　alignment.　Such　incomplete　alignment　adversely　affects　model　training　and　retrieval　accuracy.　To　address　this　problem,　a　relevance-guided　adaptive　learning　method　is　proposed,　which　quantifies　and　leverages　the　relevance　of　image–text　pairs　to　refine　the　training　process　while　enhancing　retrieval　performance.　First,　the　proposed　method　introduces　an　image–text　relevance　measurement　mechanism　that　integrates　global　and　local　feature　distances　to　accurately　evaluate　the　degree　of　semantic　relevance　between　images　and　texts.　Second,　a　relevance-based　sample　division　strategy　is　proposed,　utilizing　a　Gaussian　Mixture　Model　to　dynamically　redivide　samples　into　positive　and　negative　pairs　according　to　the　measured　image–text　relevance.　This　strategy　refines　the　training　dataset,　reduces　noise,　and　enhances　the　effectiveness　of　model　learning.　Finally,　a　relevance-weighted　triplet　loss　is　designed　to　adaptively　adjust　the　contribution　of　sample　pairs　to　the　loss　function　based　on　their　relevance,　further　optimizing　model　training　and　enhancing　retrieval　accuracy.　Experimental　results　on　multiple　remote　sensing　image–text　retrieval　datasets　demonstrate　that　the　proposed　method　significantly　improves　retrieval　accuracy　and　performance.　©　1980-2012　IEEE.