Neuromorphic　vision　systems　have　the　capacity　to　simulate　the　perception　and　processing　of　visual　information　by　visual　cells　in　the　retina.　However,　when　confronted　with　the　challenges　posed　by　a　substantial　volume　of　complex　data　and　complex　environments,　traditional　neuromorphic　vision　systems　are　unable　to　handle　redundant　signals　of　overenhancement　and　suppression.　These　systems　are　required　to　face　the　considerable　challenges　posed　by　complicated　circuits　and　algorithms.　In　this　paper,　we　present　an　adaptive　synaptic　transistor　with　a　built-in　heterojunction　that　can　switch　between　three　modes　of　synaptic　excitation-inhibition　effect,　excitation-adaptive,　and　inhibition-adaptive　photoconductivity　effect　by　utilizing　light　switching　and　wavelength　change.　The　device　can　complete　the　entire　adaptation　process　from　excitation　sensitization　to　self-adaptation　to　the　initial　current　in　1　s,　and　from　excitation　sensitization　to　adaptation　in　3.2　s.　The　adaptation　speed　is　superior　to　that　of　the　human　eye　(5　min).　The　combination　of　convolutional　neural　networks　(CNNs)　with　adaptive　synaptic　transistors　has　yielded　the　development　of　an　advanced　neuromorphic　vision　system.　This　system　exhibits　fast　self-adaptation　and　static　image　recognition　and　classification　capabilities,　with　a　recognition　rate　that　exceeds　90%,　thereby　facilitating　the　advancement　of　next-generation　neuromorphic　vision　systems.