Deep learning has achieved great success in image classification, natural language processing, and speech recognition. Data augmentation can effectively increase the scale and diversity of training data, thereby improving the generalization of deep learning models. However, for a given dataset, a well-designed data augmentation strategy relies heavily on expert experience and domain knowledge and requires repeated attempts, which is time-consuming and labor-intensive. In recent years, automated data augmentation has attracted widespread attention from the academic community and the industry through the automated design of data augmentation strategies. To solve the problem that existing automated data augmentation algorithms cannot strike a good balance between prediction accuracy and search efficiency, this study proposes an efficient automated data augmentation algorithm SGES AA based on a self-guided evolution strategy. First, an effective continuous vector representation method is designed for the data augmentation strategy, and then the automated data augmentation problem is converted into a search problem of continuous strategy vectors. Second, a strategy vector search method based on the self-guided evolution strategy is presented. By introducing historical estimation gradient information to guide the sampling and updating of exploration points, it can effectively avoid the local optimal solution while improving the convergence of the search process. The results of extensive experiments on image, text, and speech datasets show that the proposed algorithm is superior to or matches the current optimal automated data augmentation methods without significantly increasing the time consumption of searches.