Abstract: Due to the soft material properties of flexible robotic arms, they are highly susceptible to environmental uncertainties, leading to unexpected deformations that affect control accuracy. To address this issue, this paper introduces a cerebellar spiking neural network model inspired by the human cerebellum's regulation of movement and adaptability to the environment, aiming to correct the motion behavior of flexible robotic arms under environmental interference. First, a multi-degree-of-freedom flexible robotic arm model is established using a segmented constant curvature method. This model features both movable and rotational joints, enabling stretching and bending motions. The inverse kinematics model of the robotic arm is then approximated using a sequential quadratic programming algorithm to compute the joint parameters corresponding to the desired trajectory. Next, drawing inspiration from the cerebellar cortex's neural system structure and adaptive function, the synaptic plasticity between the granule layer and Purkinje cell layer is modeled to fully construct the cerebellar spiking neural network model. Finally, the paper investigates the motion performance of the flexible robotic arm in completing circular and "figure-eight" trajectories under environmental interference. It is found that, compared to open-loop control without the cerebellar model, the trajectory error of the robotic arm's end-effector under the control of cerebellar spiking neural network is reduced by 95% and 96%, respectively. This 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 offers greater biological interpretability and provides a brain-inspired intelligent method for controlling flexible robotic arms under uncertain disturbances.