Memristor-based　physical　reservoir　computing　holds　significant　potential　for　efficiently　processing　complex　spatiotemporal　data,　which　is　crucial　for　advancing　artificial　intelligence.　However,　owing　to　the　single　physical　node　mapping　characteristic　of　traditional　memristor　reservoir　computing,　it　inevitably　induces　high　repeatability　of　eigenvalues　to　a　certain　extent　and　significantly　limits　the　efficiency　and　performance　of　memristor-based　reservoir　computing　for　complex　tasks.　Hence,　this　work　firstly　reports　an　artificial　light-emitting　synaptic　(LES)　device　with　dual　photoelectric　output　for　reservoir　computing,　and　a　reservoir　system　with　mixed　physical　nodes　is　proposed.　The　system　effectively　transforms　the　input　signal　into　two　eigenvalue　outputs　using　a　mixed　physical　node　reservoir　comprising　distinct　physical　quantities,　namely　optical　output　with　nonlinear　optical　effects　and　electrical　output　with　memory　characteristics.　Unlike　previously　reported　memristor-based　reservoir　systems,　which　pursue　rich　reservoir　states　in　one　physical　dimension,　our　mixed　physical　node　reservoir　system　can　obtain　reservoir　states　in　two　physical　dimensions　with　one　input　without　increasing　the　number　and　types　of　devices.　The　recognition　rate　of　the　artificial　light-emitting　synaptic　reservoir　system　can　achieve　97.22%　in　MNIST　recognition.　Furthermore,　the　recognition　task　of　multichannel　images　can　be　realized　through　the　nonlinear　mapping　of　the　photoelectric　dual　reservoir,　resulting　in　a　recognition　accuracy　of　99.25%.　The　mixed　physical　node　reservoir　computing　proposed　in　this　work　is　promising　for　implementing　the　development　of　photoelectric　mixed　neural　networks　and　material-algorithm　collaborative　design.　This　manuscript　proposes　a　photoelectric　dual-output　mixed　physical　node　reservoir　system.　It　achieves　higher　handwriting　digit　recognition　accuracy　and　use　the　photoelectric　output　characteristics　to　achieve　multichannel　image　recognition.