Cross-modal　hashing　retrieval　has　attracted　extensive　attention　due　to　its　low　storage　requirements　as　well　as　high　retrieval　efficiency.　In　particular,　how　to　more　fully　exploit　the　correlation　of　different　modality　data　and　generate　a　more　distinguished　representation　is　the　key　to　improving　the　performance　of　this　method.　Moreover,　Transformer-based　models　have　been　widely　used　in　various　fields,　including　natural　language　processing,　due　to　their　powerful　contextual　information　processing　capabilities.　Based　on　these　motivations,　we　propose　a　Transformer-based　Distinguishing　Strong　Representation　Deep　Hashing　(TDSRDH).　For　text　modality,　since　the　sequential　relations　between　words　imply　semantic　relations　that　are　not　independent　relations,　we　thoughtfully　encode　them　using　a　transformer-based　encoder　to　obtain　a　strong　representation.　In　addition,　we　propose　a　triple-supervised　loss　based　on　the　commonly　used　pairwise　loss　and　quantization　loss.　The　latter　two　ensure　the　learned　features　and　hash-codes　can　preserve　the　similarity　of　the　original　data　during　the　learning　process.　The　former　ensures　that　the　distance　between　similar　instances　is　closer　and　the　distance　between　dissimilar　instances　is　farther.　So　that　TDSRDH　can　generate　more　discriminative　representations　while　preserving　the　similarity　between　modalities.　Finally,　experiments　on　the　three　datasets　MIRFLICKR-25K,　IAPR　TC-12,　and　NUS-WIDE　demonstrated　the　superiority　of　TDSRDH　over　the　other　baselines.　Moreover,　the　effectiveness　of　the　proposed　idea　was　demonstrated　by　ablation　experiments.