In order to reduce the tracking error of hydraulic actuators for single rod hydraulic cylinders in excavators, a fuzzy radial basis function（RBF）neural network proportional-integral-derivative（PID）control system based on differential evolution algorithm optimization is proposed. The displacement signal tracking error of hydraulic actuators is verified through simulation. Create a schematic diagram of the hydraulic circuit of a single rod hydraulic cylinder, explain the working principle of the excavator hydraulic actuator, and define the load dynamics equation of the hydraulic actuator. Constructing a fuzzy RBF neural network PID control model and introducing differential evolution algorithm as an optimization method can quickly search for the optimal parameters of the RBF neural network PID control system, thereby achieving precise control of excavator hydraulic actuators. Using Matlab software to simulate the displacement signal of a single rod hydraulic actuator and analyze the error size before and after optimization. The results show that before optimization, the tracking error of the displacement signal of the single rod hydraulic actuator is relatively large. After optimization, the tracking error of the displacement signal of the single rod hydraulic actuator is relatively small. The proposed differential evolution algorithm optimizes the fuzzy RBF neural network PID control system, which can enhance the anti-load disturbance ability of the single rod hydraulic actuator, thereby reducing the tracking error of the single rod hydraulic actuator.