There　are　huge　differences　in　data　distribution　and　feature　representation　of　different　modalities.　How　to　flexibly　and　accurately　retrieve　data　from　different　modalities　is　a　challenging　problem.　The　mainstream　common　subspace　methods　only　focus　on　the　heterogeneity　gap,　and　use　a　unified　method　to　jointly　learn　the　common　representation　of　different　modalities,　which　can　easily　lead　to　the　difficulty　of　multi-modal　unified　fitting.　In　this　work,　we　innovatively　propose　the　concept　of　multi-modal　information　density　discrepancy,　and　propose　a　modality-specific　adaptive　scaling　method　incorporating　prior　knowledge,　which　can　adaptively　learn　the　most　suitable　network　for　different　modalities.　Secondly,　for　the　problem　of　efficient　semantic　fusion　and　interference　features,　we　propose　a　multi-level　modal　feature　attention　mechanism,　which　realizes　the　efficient　fusion　of　text　semantics　through　attention　mechanism,　explicitly　captures　and　shields　the　interference　features　from　multiple　scales.　In　addition,　to　address　the　bottleneck　of　cross-modal　retrieval　task　caused　by　the　insufficient　quality　of　multimodal　common　subspace　and　the　defects　of　Transformer　structure,　this　paper　proposes　a　cross-level　interaction　injection　mechanism　to　fuse　multi-level　patch　interactions　without　affecting　the　pre-trained　model　to　construct　higher　quality　latent　representation　spaces　and　multimodal　common　subspaces.　Comprehensive　experimental　results　on　four　widely　used　cross-modal　retrieval　datasets　show　the　proposed　MASAN　achieves　the　state-of-the-art　results　and　significantly　outperforms　other　existing　methods.