Neuromorphic　computing　has　been　a　candidate　to　work　like　human　brains　to　deal　with　big　data　energy-efficiently　beyond　the　von　Neumann　architecture.　As　an　important　part,　artificial　synapses　based　on　waveguides　with　phase-change　materials　(PCMs)　play　a　critical　role　in　neural　networks.　However,　because　of　demanding　conditions　of　triggering　intermediate　phase　states　to　achieve　multilevel　weights　it　is　relatively　difficult　to　obtain　accurate　multilevel　weights.　In　this　work,　we　designed　a　photonic　synapse　based　on　slot-ridge　waveguides　with　multi-block　Ge2Sb2Te5　(GST)　to　precisely　and　easily　get　multilevel　weights　by　triggering　the　specific　number　of　GST　islands　between　crystalline　and　amorphous　states.　The　properties　of　the　GST　material　were　analyzed　by　Raman　spectroscopy　and　spectroscopic　ellipsometry　and　the　nonlinearity　factor　of　photonic　synaptic　weights　was　optimized　for　the　number　of　GST　islands.　The　recognition　tasks　of　MNIST　and　the　optical　recognition　of　NIST　were　performed　by　multilayer　perceptron,　and　the　optimal　accuracy　of　the　optical　recognition　of　NIST　was　92.7　%　for　5　GST　islands.　The　accuracy　of　MNIST　was　improved　by　2　%　for　5　GST　islands　compared　with　that　of　2　GST　islands.　Besides,　the　electro-thermal　phase　transition　conditions　of　crystallization　and　reamorphization　were　also　obtained　by　simulation.　This　work　will　open　the　way　to　precisely　and　easily　achieve　photonic　synapses　with　multilevel　weights　in　the　future.　©　2023　Elsevier　B.V.