Photonic　synaptic　transistors　are　promising　neuromorphic　computing　systems　that　are　expected　to　circumvent　the　intrinsic　limitations　of　von　Neumann-based　computation.　The　design　and　construction　of　photonic　synaptic　transistors　with　a　facile　fabrication　process　and　high-efficiency　information　processing　ability　are　highly　desired,　while　it　remains　a　tremendous　challenge.　Herein,　a　new　approach　based　on　spin　coating　of　a　blend　of　CsPbBr3　perovskite　quantum　dot　(QD)　and　PDVT-10　conjugated　polymer　is　reported　for　the　fabrication　of　photonic　synaptic　transistors.　The　combination　of　flat　surface,　outstanding　optical　absorption,　and　remarkable　charge　transporting　performance　contributes　to　high-efficiency　photon-to-electron　conversion　for　such　perovskite-based　synapses.　High-performance　photonic　synaptic　transistors　are　thus　fabricated　with　essential　synaptic　functionalities,　including　excitatory　postsynaptic　current　(EPSC),　paired-pulse　facilitation　(PPF),　and　long-term　memory.　By　utilizing　the　photonic　potentiation　and　electrical　depression　features,　perovskite-based　photonic　synaptic　transistors　are　also　explored　for　neuromorphic　computing　simulations,　showing　high　pattern　recognition　accuracy　of　up　to　89.98%,　which　is　one　of　the　best　values　reported　so　far　for　synaptic　transistors　used　in　pattern　recognition.　This　work　provides　an　effective　and　convenient　pathway　for　fabricating　perovskite-based　neuromorphic　systems　with　high　pattern　recognition　accuracy.