Aiming at the problem that the universal perturbation search in HGAA （Hyperspherical General Adversarial Attacks） algorithm is always limited to the spatial spherical surface， and it does not have the ability to search the space inside the sphere， a differential evolution algorithm based on hypersphere was proposed. In the algorithm， the search space was expanded to the interior of the sphere， and Differential Evolution （DE） algorithm was used to search the optimal sphere， so as to generate universal perturbations with higher fooling rate and lower modulus length on this sphere. Besides， the influence of key parameters such as the number of populations on the algorithm was analyzed， and the performance of the universal perturbations generated by the algorithm on different neural network models was tested. The algorithm was verified on CIFAR10 and SVHN image classification datasets， and the fooling rate of the algorithm was increased by up to 11.8 percentage points compared with that of HGAA algorithm. Experimental results show that this algorithm extends the universal perturbation search space of the HGAA algorithm， reduces the modulus length of universal perturbation， and improves the fooling rate of universal perturbations.