This paper presents a novel probability theory-based method for calculating the cyclical degree of freedom (DOF) of mechanisms, addressing the limitations of traditional deterministic approaches in determining cyclical DOF of complex parallel mechanisms and uncertain configurations. By integrating random sampling and significance testing, the method enables statistical evaluation of DOF across the entire motion range, ensuring reliability through binomial distribution-based hypothesis testing. The approach uses the Jacobian matrix’s null space and closed-loop constraint equations to model mechanism kinematics, and iteratively optimizes samples to achieve statistically significant results. Case studies on single-loop and multi-loop mechanisms validate the accuracy, showing consistent results with modified G-K formula calculations. This framework enhances computational efficiency by reducing manual complexity and offers universality for automated DOF analysis, making it particularly suitable for complex mechanical systems with singular configurations or redundant constraints. It paves the way for integrating statistical DOF analysis into computer-aided design workflows to support data-driven mechanism design and optimization.