The difficulty in applying Spiking Neural Networks (SNNs) on chips poses a significant challenge. To alleviate this, integrating Model-Based Design (MBD) principles alongside techniques like multi-software integration and automatic code generation, the idea of piecewise approximation to ideal curves was adopted. A reasonable synaptic response curve was obtained through MATLAB/Simulink software simulation. A forward propagation channel model was established, and the adjustment of model weights was achieved through a developed supervised learning Tempotron algorithm. Employing the STM32-MAT/TARGET toolbox, an STM32 embedded system diagram was constructed, and the STM32CubeMX software was utilized to initialize the STM32 chip, with both components debugged jointly to generate a C language program. Functional programs were written using Keil software, simulated in Proteus software, ultimately leading to the successful implementation of the SNN propagation model on the mainstream MCU chip, STM32.