Rolling bearing is one of the most important components of rotating machinery systems to ensure safe operation. It is important to carry out studies on rolling bearing feature recognition for theoretical and practical application. The commonly used deep learning rolling bearing feature recognition methods require supervised labeled data or unsupervised fault data to participate in the training, and labels of data and fault data are not easily accessible to meet the rolling bearing feature recognition requirements. This article proposes an edge computing method for differential evolution of generative adversarial networks rolling bearing feature recognition, namely the EC-DE method. The training process uses only healthy data to train the generative adversarial networks and learn the distribution pattern of healthy data. The edge node compares the distribution difference between the input samples and the generative samples of generative adversarial networks for identification and exits early according to the health confidence level to improve the system′ s real-time performance. The cloud node uses a differential evolution algorithm to search the generator latent space of the generative adversarial networks to obtain the latent variables corresponding to the input samples, which improves the recognition accuracy. The proposed method achieves 99. 8% accuracy on CWRU rolling bearing public data set and is insensitive to hyper-parameters, and the inference stage takes less time, which is valuable for a practical production application.