Abstract: Provided are a method and apparatus for generating a deep ensemble model for image classification, and a computer device. The method includes: randomly sampling, from a search space of classical convolutional neural network (CNN) models for image classification, a neural architecture of a first group of CNN models; collecting representative image samples based on a scenario of an image classification task, marking the collected image samples with classification labels, constructing a training dataset based on the marked image samples, and training the sampled first group of CNN models based on the training dataset and evaluating image classification performance thereof; generating a second group of CNN models based on a surrogate model and the trained first group of CNN models; and constructing a structure of a deep ensemble model for image classification based on the second group of CNN models and a multi-objective optimization strategy.