This study addresses the issues of complexity in greenhouse operational environments and poor stability of existing mechanical walking systems by conducting research on the autonomous following electric platform walking speed control in greenhouses. Due to the system’s inherent nonlinearity and time-varying characteristics, traditional PID control algorithms fail to achieve effective control. Therefore, a dung beetle optimizer (DBO) optimized BP neural network PID control algorithm was proposed. This algorithm optimized the weights of the BP neural network by using the DBO algorithm, thereby accelerating the self-learning rate of the BP neural network. It achieved rapid and precise control of the greenhouse autonomous following electric platform walking speed, enhanced system response speed, and reduced overshoot. Experimental results demonstrated that at a walking speed of 1 m / s, the system exhibited an average response speed of 0.11 s, settling time of 0.27 s, and a maximum overshoot of 2.44% . When there were changes in track speed and direction, the system maintained advantages of fast response, minimal overshoot, and oscillation-free steady-state process. Compared with PID control algorithm, BP-PID control algorithm, GA-PID control algorithm, ACO-PID control algorithm, the DBO-BP-PID control algorithm showed superior performance in control stability and precision, effectively mitigating system hysteresis and nonlinear effects, thereby meeting the control requirements for greenhouse autonomous following electric platform walking speed.