In nuclear power plant reliability and risk analysis, Common Cause Failure (CCF) is a critical aspect. Several CCF parameter models, including the Basic Parameter Model, α-factor model, MGL model, and β-factor model, are employed to assess CCF risks. However, CCF events are rare, and Probabilistic Risk Assessment (PRA) often depends on external data, leading to discrepancies between parameter models. This paper reviews commonly used CCF models and examines the relationships for converting parameters to enhance their compatibility and application in PRA. The derived framework simplifies the conversion process between the α-factor, MGL, and β-factor models, offering a systematic approach to ensure consistency in reliability assessments. This framework facilitates seamless conversion between different models and provides a more efficient and unified theoretical support for future PRA applications. It reduces uncertainty and differences between models, improving accuracy in nuclear power plant risk assessments. By adopting this framework, practitioners can enhance the reliability and consistency of risk evaluation, making it more adaptable to various real-world scenarios in nuclear safety.