Due to the fast advancement in wireless communication technology, the demand for the modeling and generating of wireless channels is increasing. Deep learning technology is gradually applied in the wireless communication field, and the Generative Adversarial Network (GAN) framework provides a new solution for channel modeling. This paper presents a method based on Wasserstein GAN with gradient penalty (WGAN-GP) guided by an attention mechanism for wireless channel modeling and generating. The feature extraction capability of the model is enhanced by adding a channel attention mechanism in WGAN-GP, and the representation capability of the model is enhanced by adaptively recalibrating the channel feature response. The experimental results demonstrate that the proposed approach accurately models the channel distribution and generates data that closely aligns with the real channel distribution. The proposed method has been shown to achieve superior qualitative and quantitative evaluation compared to the existing method.