The　combination　of　artificial　neural　networks　(ANN)　and　spiking　neural　networks　(SNN)　holds　great　promise　for　advancing　artificial　general　intelligence　(AGI).　However,　the　reported　ANN　and　SNN　computational　architectures　are　independent　and　require　a　large　number　of　auxiliary　circuits　and　external　algorithms　for　fusion　training.　Here,　a　novel　vertical　bulk　heterojunction　neuromorphic　transistor　(VHNT)　capable　of　emulating　both　ANN　and　SNN　computational　functions　is　presented.　TaOx-based　electrochemical　reactions　and　PDVT-10/N2200-based　bulk　heterojunctions　are　used　to　realize　spike　coding　and　voltage　coding,　respectively.　Notably,　the　device　exhibits　remarkable　efficiency,　consuming　a　mere　0.84　nJ　of　energy　consumption　for　a　single　multiply　accumulate　(MAC)　operation　with　excellent　linearity.　Moreover,　the　device　can　be　switched　to　spiking　neuron　and　self-activation　neuron　by　simply　changing　the　programming　without　auxiliary　circuits.　Finally,　the　VHNT-based　artificial　spiking　neural　network　(ASNN)　fusion　simulation　architecture　is　demonstrated,　achieving　95%　accuracy　for　Canadian-Institute-For-Advanced-ResearchResearch-10　(CIFARResearch-10)　dataset　while　significantly　enhancing　training　speed　and　efficiency.　This　work　proposes　a　novel　device　strategy　for　developing　high-performance,　low-power,　and　environmentally　adaptive　AGI.