Abstract: Based on a traditional machine learning algorithm (XGBoost), a deep learning algorithm (CU-Net), and the winter wind speed data from 10 m near the ground with a resolution of 100 m, this paper studied and compared the correction methods for wind speed forecast deviation in the mountainous stations and surrounding areas of the Yanqing and Zhangjiakou competition areas (Beijing Winter Olympic Games) using the rapid-refresh integrated seamless ensemble (RISE) system. For station correction, the 10-m wind speed predicted by the RISE system is interpolated to the corresponding automatic weather station. Subsequently, a separate XGBoost model is constructed for each classification according to the wind speed rating table. Afterward, each interval model was combined to form L-XGBoost, using the root mean square error and forecast accuracy as its scoring standard. Investigations revealed that the correction effect of the L-XGBoost algorithm for wind speed classification was better than the original XGBoost model without classification, indicating that introducing a classification method to traditional machine learning helped improve the wind speed prediction skills of the complex mountain stations. Subsequently, for the wind speed correction of the station and its surrounding areas based on the CU-Net model, this paper constructed a new algorithm model (CU-Net++) by introducing the CU-Net sub-networks with different depths, considering the influence of daily forecast errors and complex terrains on the 10-m wind speed. This paper also constructed spatial small-area sample data, considering the automatic weather station as the center, to correct the wind speed prediction deviation of the RISE system. The test results indicated that although both CU-Net and CU-Net++ fully mined the wind field change rules in time and space dimensions, the wind speed correction results of the CU-Net++ model performed better than those of the CU-Net model, effectively reducing the grid wind speed prediction error of RISE products. Hence, introducing prediction error and complex terrain plays an important positive role in the deviation correction of a surface 10 m wind speed-based investigation.