In recent years, deep neural network (DNN) has made great progress in the field of image. However, studies show that DNN is susceptible to the interference of adversarial examples and exhibits poor robustness. By generating adversarial examples to attack DNN, the robustness of DNN can be evaluated, and then corresponding defense methods can be adopted to improve the robustness of DNN. The existing adversarial example generation methods still have some defects, such as insufficient sparsity of generated perturbations, and excessive perturbation magnitude. This study proposes an adversarial example generation method based on sparse perturbation, sparse perturbation based adversarial example generation (SparseAG), which can generate relatively sparse and small-magnitude perturbations for image examples. Specifically, SparseAG first selects the perturbation points iteratively based on the gradient value of the loss function for the input image to generate the initial adversarial example. In each iteration, the candidate set of the new perturbation points is determined in the order of gradient value from large to small values, and the perturbation which makes the value of loss function value smallest is added to the image. Secondly, a perturbation optimization strategy is employed in the initial perturbation scheme to improve the sparsity and authenticity of the adversarial example. The perturbations are improved based on the importance of each perturbation for jumping out of the local optimum, and the redundant perturbation and the redundant perturbation magnitude are further reduced. This study selects the CIFAR-10 dataset and the ImageNet dataset to evaluate the method in the target attack and non-target attack scenarios. The experimental results show that SparseAG can achieve a 100% attack success rate in different datasets and different attack scenarios, and the sparsity and the overall perturbation magnitude of the generated perturbations are better than those of the comparison methods.