Understanding　gene　expression　processes　necessitates　the　accurate　classification　and　identification　of　transcription　factors,　which　is　supported　by　high-throughput　sequencing　technologies.　However,　these　techniques　suffer　from　inherent　limitations　such　as　time　consumption　and　high　costs.　To　address　these　challenges,　the　field　of　bioinformatics　has　increasingly　turned　to　deep　learning　technologies　for　analyzing　gene　sequences.　Nevertheless,　the　pursuit　of　improved　experimental　results　has　led　to　the　inclusion　of　numerous　complex　analysis　function　modules,　resulting　in　models　with　a　growing　number　of　parameters.　To　overcome　these　limitations,　it　is　proposed　a　novel　approach　for　analyzing　DNA　transcription　factor　sequences,　which　is　named　as　DeepCAC.　This　method　leverages　deep　convolutional　neural　networks　with　a　multi-head　self-attention　mechanism.　By　employing　convolutional　neural　networks,　it　can　effectively　capture　local　hidden　features　in　the　sequences.　Simultaneously,　the　multi-head　self-attention　mechanism　enhances　the　identification　of　hidden　features　with　long-distant　dependencies.　This　approach　reduces　the　overall　number　of　parameters　in　the　model　while　harnessing　the　computational　power　of　sequence　data　from　multi-head　self-attention.　Through　training　with　labeled　data,　experiments　demonstrate　that　this　approach　significantly　improves　performance　while　requiring　fewer　parameters　compared　to　existing　methods.　Additionally,　the　effectiveness　of　our　approach　is　validated　in　accurately　predicting　DNA　transcription　factor　sequences.