The　rapid　development　of　neuromorphic　computing　has　stimulated　extensive　research　interest　in　artificial　synapses.　Optoelectronic　artificial　synapses　using　laser　beams　as　stimulus　signals　have　the　advantages　of　broadband,　fast　response,　and　low　crosstalk.　However,　the　optoelectronic　synapses　usually　exhibit　short　memory　duration　due　to　the　low　lifetime　of　the　photo-generated　carriers.　It　greatly　limits　the　mimicking　of　human　perceptual　learning,　which　is　a　common　phenomenon　in　sensory　interactions　with　the　environment　and　practices　of　specific　sensory　tasks.　Herein,　a　heterostructure　optoelectronic　synapse　based　on　graphene　nanowalls　and　CsPbBr3　quantum　dots　was　fabricated.　The　graphene/CsPbBr3　heterojunction　and　the　natural　middle　energy　band　in　graphene　nanowalls　extend　the　carrier　lifetime.　Therefore,　a　long　half-life　period　of　photocurrent　decay　-　35.59　s　has　been　achieved.　Moreover,　the　long-term　optoelectronic　response　can　be　controlled　by　the　adjustment　of　numbers,　powers,　wavelengths,　and　frequencies　of　the　laser　pulses.　Next,　an　artificial　neural　network　consisting　of　a　28　x　28　synaptic　array　was　established.　It　can　be　used　to　mimic　a　typical　characteristic　of　human　perceptual　learning　that　the　ability　of　sensory　systems　is　enhanced　through　a　learning　experience.　The　learning　behavior　of　image　recognition　can　be　tuned　based　on　the　photocurrent　response　control.　The　accuracy　of　image　recognition　keeps　above　80%　even　under　a　low-frequency　learning　process.　We　also　verify　that　less　time　is　required　to　regain　the　lost　sensory　ability　that　has　been　previously　learned.　This　approach　paves　the　way　toward　high-performance　intelligent　devices　with　controllable　learning　of　visual　perception.