Because wind power has high volatility and uncertainty, it may bring many problems and challenges to the Unit Commitment (UC) problems. Therefore, a scenario analysis method based on the sequence Kriging model is proposed to solve wind power uncertainty. It generates a large number of scenarios by a “forecast bin” method. Then the operational cost of the corresponding scenarios is estimated by a sequence optimization Kriging model. At the same time, an important sampling method is adopted to reduce scenarios given the influence of wind uncertainty combined with the operational cost. The effectiveness of this method is verified by a stochastic unit commitment model considering power balance and wind ramping constraints. It is shown that the sequence optimization Kriging model can use fewer points to estimate the operational cost of the scenario set. Compared to the Kantorovich distance method, the result of the proposed method is more representative, and the resulting solution has better economy and reliability. This work is supported by Youth Science Fund of National Natural Science Foundation of China (No. 51607111).