Real-time and precise assessment of airport runway surface conditions in winter is crucial for ensuring aircraft takeoff and landing safety, as well as enhancing airport operational efficiency. Given that traditional pavement assessment models generally rely on fixed weights and struggle to effectively account for the randomness and fuzziness of assessment indicators, a comprehensive evaluation model based on a normal cloud model and variable weight theory is proposed. A multi-dimensional assessment index system is constructed, and membership functions are generated using the normal cloud model to effectively integrate the fuzziness and randomness of the indicators. To overcome the limitations of traditional methods that rely heavily on subjective experience, a combined weighting model integrating the Analytic Hierarchy Process and the Entropy Weight Method is introduced to determine the constant base weights. Furthermore, variable weight theory is applied to construct a dynamic weight matrix that automatically adjusts weights, thereby emphasizing the impact of abnormal indicators on the comprehensive assessment results. The runway condition grade is then determined based on the principle of maximum membership degree. Validation using actual runway operational data demonstrates that, compared to the constant weight model, the proposed method exhibits outstanding abnormal risk capture capability under complex conditions with multi-factor coupling. In scenarios such as static contaminant sample evaluation and continuous monitoring of dynamic conditions, the model demonstrates superior accuracy in risk warning and sensitivity in dynamic response, providing a more scientific decision-making basis for winter pavement condition evaluation.