Time‐frequency analysis based on Wigner‐Ville distribution (WVD) plays a significant role in analysing non‐stationary signals, but it is susceptible to interference from cross‐terms (CTs) for multi‐component signals. To address this issue, a novel WVD enhancement method based on generative adversarial networks (namely WVD‐GAN) is proposed, to achieve highly‐concentrated time‐frequency (TF) representation. Specifically, a deep feature extraction module is designed with multiple residual connections in the generator of WVD‐GAN to leverage the latent information encoded in the shallow representations. Meanwhile, a simple and effective attention module is introduced to enhance auto‐term features. Moreover, a multi‐scale discriminator is proposed based on dilated convolutions to guide the generator to reconstruct high‐resolution TF images by discriminating CT. Finally, a comparative analysis is provided to demonstrate the effectiveness and robustness of the proposed method on different simulated and real‐life datasets. Extensive experiments demonstrate that the proposed method outperforms several state‐of‐the‐art methods. A novel approach is presented for Wigner‐Ville distribution enhancement based on generative adversarial networks to achieve high‐resolution time‐frequency representation. The authors’ method utilises the Wigner‐Ville distribution method to generate time‐frequency images of non‐stationary signals. Then a Wigner‐Ville distribution enhancement model based generative adversarial networks is proposed to construct highly‐concentrated time‐frequency representations. Extensive experiments demonstrate that the proposed method achieves high‐resolution time‐frequency images and can effectively restore the instantaneous frequency trajectory of non‐stationary signals, while maintaining energy concentration.