Neural　machine　translation,　which　has　an　encoder-decoder　framework,　is　considered　to　be　a　feasible　way　for　future　machine　translation.　Nevertheless,　with　the　fusion　of　multiple　languages　and　the　continuous　emergence　of　new　words,　most　current　neural　machine　translation　systems　based　on　von　Neumann’s　architecture　have　seen　a　substantial　increase　in　the　number　of　devices　for　the　decoder,　resulting　in　high-energy　consumption　rate.　Here,　a　multilevel　photosensitive　blending　semiconductor　optoelectronic　synaptic　transistor　(MOST)　with　two　different　trapping　mechanisms　is　firstly　demonstrated,　which　exhibits　8　stable　and　well　distinguishable　states　and　synaptic　behaviors　such　as　excitatory　postsynaptic　current,　short-term　memory,　and　long-term　memory　are　successfully　mimicked　under　illumination　in　the　wavelength　range　of　480–800　nm.　More　importantly,　an　optical　decoder　model　based　on　MOST　is　successfully　fabricated,　which　is　the　first　application　of　neuromorphic　device　in　the　field　of　neural　machine　translation,　significantly　simplifying　the　structure　of　traditional　neural　machine　translation　system.　Moreover,　as　a　multi-level　synaptic　device,　MOST　can　further　reduce　the　number　of　components　and　simplify　the　structure　of　the　codec　model　under　light　illumination.　This　work　first　applies　the　neuromorphic　device　to　neural　machine　translation,　and　proposes　a　multi-level　synaptic　transistor　as　the　based　cell　of　decoding　module,　which　would　lay　the　foundation　for　breaking　the　bottleneck　of　machine　translation.　[Figure　not　available:　see　fulltext.]　©　2022,　Science　China　Press　and　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.