Temporal　dynamic　graphs　(TDGs),　representing　the　dynamic　evolution　of　entities　and　their　relationships　over　time　with　intricate　temporal　features,　are　widely　used　in　various　real-world　domains.　Existing　methods　typically　rely　on　mainstream　techniques　such　as　transformers　and　graph　neural　networks　(GNNs)　to　capture　the　spatiotemporal　information　of　TDGs.　However,　despite　their　advanced　capabilities,　these　methods　often　struggle　with　significant　computational　complexity　and　limited　ability　to　capture　temporal　dynamic　contextual　relationships.　Recently,　a　new　model　architecture　called　mamba　has　emerged,　noted　for　its　capability　to　capture　complex　dependencies　in　sequences　while　significantly　reducing　computational　complexity.　Building　on　this,　we　propose　a　novel　method,　TDG-mamba,　which　integrates　mamba　for　TDG　learning.　TDG-mamba　introduces　deep　semantic　spatiotemporal　embeddings　into　the　mamba　architecture　through　a　specially　designed　spatiotemporal　prior　tokenization　module　(SPTM).　Furthermore,　to　better　leverage　temporal　information　differences　and　enhance　the　modeling　of　dynamic　changes　in　graph　structures,　we　separately　design　a　bidirectional　mamba　and　a　directed　GNN　for　improved　spatiotemporal　embedding　learning.　Link　prediction　experiments　on　multiple　public　datasets　demonstrate　that　our　method　delivers　superior　performance,　with　an　average　improvement　of　5.11%　over　baseline　methods　across　various　settings.