Faced　with　a　huge　amount　of　information,　the　brain　relies　on　attention　mechanisms　to　highly　select　information　for　efficient　processing.　The　degree　of　processing　capacity　required　for　information　in　different　scenarios　also　varies,　and　the　brain　relies　on　attention　to　significantly　enhance　or　weaken　its　ability　to　process　information.　However,　many　studies　on　attention　only　focus　on　the　behavioral　differences　between　attention　and　no　attention,　without　further　research　on　the　degree　of　regulation　of　attention.　The　application　scenarios　of　artificial　vision　systems　are　so　complex　that　we　need　to　regulate　the　degree　of　attention　to　cope　with　different　information　processing　intensities.　In　this　work,　we　demonstrated　an　optoelectronic　synaptic　transistor　based　on　the　mixture　of　PDVT-10　and　MXene-TiO2,　and　for　the　first　time　imitated　the　mechanism　of　attention　regulation　signals　at　the　biological　level.　Based　on　the　attention　mechanism,　for　slow-moving　objects,　we　enhance　the　ability　of　data　processing　to　prevent　data　loss　caused　by　undersampling,　and　for　fast-moving　objects,　we　weaken　the　ability　of　data　processing　to　prevent　data　redundancy　caused　by　oversampling.　In　addition,　our　device　array　determines　the　location　of　moving　targets　and　sends　them　to　the　YOLO　network　for　dynamic　target　detection.　Our　research　results　show　that　the　device　realizes　a　hierarchical　response　to　adapt　to　objects　with　different　motion　speeds,　which　expands　the　application　scenarios　of　optoelectronic　synapses.　Faced　with　a　huge　amount　of　information,　the　brain　relies　on　attention　mechanisms　to　highly　select　information　for　efficient　processing.