Remote-sensing　fine-grained　ship　classification　(RS-FGSC)　poses　a　significant　challenge　due　to　the　high　similarity　between　classes　and　the　limited　availability　of　labeled　data,　limiting　the　effectiveness　of　traditional　supervised　classification　methods.　Recent　advancements　in　large　pretrained　vision-language　models　(VLMs)　have　demonstrated　impressive　capabilities　in　few-shot　or　zero-shot　learning,　particularly　in　understanding　image　content.　This　study　delves　into　harnessing　the　potential　of　VLMs　to　enhance　classification　accuracy　for　unseen　ship　categories,　which　holds　considerable　significance　in　scenarios　with　restricted　data　due　to　cost　or　privacy　constraints.　Directly　fine-tuning　VLMs　for　RS-FGSC　often　encounters　the　challenge　of　overfitting　the　seen　classes,　resulting　in　suboptimal　generalization　to　unseen　classes,　which　highlights　the　difficulty　in　differentiating　complex　backgrounds　and　capturing　distinct　ship　features.　To　address　these　issues,　we　introduce　a　novel　prompt　tuning　technique　that　employs　a　hierarchical,　multigranularity　prompt　design.　Our　approach　integrates　remote　sensing　ship　priors　through　bias　terms,　learned　from　a　small　trainable　network.　This　strategy　enhances　the　model＇s　generalization　capabilities　while　improving　its　ability　to　discern　intricate　backgrounds　and　learn　discriminative　ship　features.　Furthermore,　we　contribute　to　the　field　by　introducing　a　comprehensive　dataset,　FGSCM-52,　significantly　expanding　existing　datasets　with　more　extensive　data　and　detailed　annotations　for　less　common　ship　classes.　Extensive　experimental　evaluations　demonstrate　the　superiority　of　our　proposed　method　over　current　state-of-the-art　techniques.　The　source　code　will　be　made　publicly　available.