Boundary　detection　is　a　challenging　problem　in　Temporal　Action　Detection　(TAD).　While　transformer-based　methods　achieve　satisfactory　results　by　incorporating　self-attention　mechanisms　to　model　global　dependencies　for　boundary　detection,　they　face　two　key　issues.　Firstly,　they　lack　explicit　learning　of　local　relationships;　this　limitation　results　in　imprecise　boundary　detection　when　subtle　appearance　changes　occur　between　adjacent　clips.　Secondly,　transformer-based　methods　lead　to　feature　convergence　across　multiple　actions　due　to　the　self-attention　mechanism＇s　tendency　to　distribute　focus　across　the　entire　input　video,　resulting　in　the　prediction　of　imprecisely　overlapping　actions.　To　address　these　challenges,　we　introduce　the　ConvTransformer　Attention　Network　(CTAN),　a　novel　framework　comprised　of　two　primary　components:　(1)　The　Temporal　Attention　Block　(TAB),　a　temporal　attention　mechanism　designed　to　emphasize　critical　temporal　positions　enriched　with　essential　action-related　features.　(2)　The　ConvTransformer　Block　(CTB),　which　employs　a　hybrid　structure　for　capturing　nuanced　appearance　changes　locally　and　action　transitions　globally.　Facilitated　with　these　components,　CTAN　is　adept　at　focusing　on　motion　features　between　overlapping　actions,　and　precisely　capturing　both　local　differences　between　adjacent　clips　and　global　action　transitions.　The　extensive　experiments　on　multiple　datasets,　including　THUMOS14,　MultiTHUMOS,　and　ActivityNet,　confirm　the　effectiveness　of　CTAN.