During a grid side fault, the operating status of each unit protection circuit in DFIG-based wind farms is the main factor affecting the transient response characteristics of point of connection. It is difficult to express the operational status of the protection circuit of DFIG mathematically. Thus an equivalent modeling method of DFIG-based wind farms based on enhanced recognition of the protection circuit operating state is proposed. First, a core sample index is constructed to extract core samples according to the influence of samples on the model training process, and the core samples are trained and enhanced by a generative adversarial network (GAN). Then, the enhanced sample set is used to train a deep belief network (DBN) to construct the operational state recognition model of the DFIG-based wind turbine protection circuit. Finally, according to the recognition results and wind speed, the units in the DFIG-based wind farms are divided into groups, and the equivalent parameters of each group are calculated to establish the equivalent model of DFIG-based wind farms. Through comparative tests and index evaluation, the effectiveness of sample augmentation in improving model training and the accuracy of the equivalent model constructed using the proposed method are verified.