Traditional　Von-Neumann　computers　would　not　meet　the　needs　of　storage　and　processing　a　large　amount　of　information　in　the　era　of　artificial　intelligence　owing　to　the　separated　storage　and　processing　unit.　Inspired　by　the　human　brain,　various　electronic　devices　have　been　developed　for　neuromorphic　computing　to　conquer　the　von　Neumann　bottleneck.　Organic　synaptic　transistors　have　attracted　increasing　interest　due　to　their　advantages　of　low　cost,　flexibility　and　ease　of　solution　fabrication.　However,　most　synaptic　transistors　based　on　the　charge　trapping　principle　use　a　single　material,　which　limits　the　adjustment　of　synaptic　plasticity.　Here,　a　novel　synaptic　device　based　on　a　hybrid　trapping　layer　was　proposed　and　investigated.　The　device　with　a　hybrid　trapping　layer　exhibits　a　larger　memory　window　than　the　device　with　a　trapping　layer　based　on　single　material,　indicating　that　the　device　with　hybrid　trapping　has　a　larger　trapping　capability.　Moreover,　our　synaptic　device　was　utilized　to　successfully　simulate　typical　synaptic　properties:　excitatory　postsynaptic　current,　inhibitory　postsynaptic　current,　paired-pulse　facilitation,　paired-pulse　depression　and　the　transition　from　short-term　plasticity　to　long-term　plasticity.　Furthermore,　an　artificial　neural　network　was　simulated　and　exhibited　a　high　recognition　accuracy.　Therefore,　the　proposed　device　could　promote　the　development　of　highly　efficient　neuromorphic　computing　systems.