Abstract: Due to the soft characteristics of its own material, the flexible robotic arm is very susceptible to the uncertainty interference in the environment, which leads to unexpected deformation and affects the control accuracy. To address this situation, this paper draws on the cerebellum's regulation of motion and adaptation to the environment in the human body, and builds a cerebellar spiking neural network model for corrective control of the motion behavior of flexible robotic arms under environmental disturbances. Firstly, a multi-degree-of-freedom flexible robotic arm model is built based on the piecewise constant curvature (PCC) method, which has moving joints and rotating joints and can realize the motion behaviors of stretching and bending; and the inverse kinematic model of the robotic arm is obtained by using the sequential quadratic programming (SQP) algorithm to approximate the computation, so as to solve the desired joint parameters corresponding to the desired trajectory. Then, drawing on the neural system structure and adaptive function of the cerebellar cortex, the synaptic plasticity rule between the granular cell layer and the Purkinje cell layer is modeled to fully construct the cerebellar spiking neural network model. Finally, this paper investigates the motion effect of the flexible robotic arm to complete the circular trajectory and the “figure-eight” trajectory under the environmental interference, and finds that compared with the direct open-loop control of the motion results without the cerebellar model, the trajectory error of the flexible robotic arm end-effector under the control of the cerebellar spiking neural network has been reduced by 95% and 96%, which demonstrates the effectiveness of the cerebellar spiking neural network model in enhancing the flexible robotic arm's resistance to uncertainty interference. Compared to traditional control methods, this approach is more biologically interpretable and provides a brain-inspired intelligent method for the motion control of flexible robotic arms under uncertainty perturbation.