Abstract: A Hellinger distance based variational evolutionary generative adversarial networks (HVE-GAN) is proposed to expand the detection dataset of “human-like” Socialbots. HVE-GAN modifies the generator of evolutionary generative adversarial networks (E-GAN) to a variational autoencoder (VAE) structure to improve the “authenticity” and diversity of the generated data, and changes the Heuristic loss function of the E-GAN generator to an improved Hellinger distance to speed up the model convergence during the training process, stabilize the gradient of the generator, and further avoid unstable training processes that affect the quality of the generated data. Comparative experimental results show that the “authenticity” and diversity of the “human-like” social robot data generated by the HVE-GAN model proposed in this paper are significantly better than the baseline models.