Convective wind is one of the common types of severe convective weather. Identification and Forecasting of con- vective wind are essential. In this paper, five kinds of features are firstly constructed from characteristics of typical convective wind-related echo phenomena based on Doppler radar data. The features include storm motion, high-value reflectivity, high-value velocity, velocity shear, and velocity texture. A severe convective wind (SCW) identification model is then built by applying the above features to the random forest model. With convective wind samples collec- ted over 13 cities of China in June–August 2016, it is found that the probability of detection (POD) of SCW is 78.9％, the false alarm ratio (FAR) is 26.4％, and the critical success index (CSI) is 61.5％. For the convective wind samples that carry typical echo features, the POD, FAR, and CSI range from 89.4％ to 99.3％, 4.2％ to 16.0％, and 76.4％ to 95.1％, respectively. Meanwhile, the POD and negative-case POD of samples without typical echo features are 66.8％ and 85.4％, respectively. The experimental results demonstrate that the SCW identification model can classify non- SCW effectively, and performs better with SCW samples carrying typical echo features than without.