Seismic noise suppression refers to a data processing technique utilized to bolster the signal-to-noise ratio of recorded seismic signals. This enhancement in clarity can significantly amplify the efficacy of subsequent analysis and processing endeavors. With the advancement of deep learning, neural networks have significantly outperformed traditional denoising methods in seismic noise reduction tasks. They can process more data in less time and achieve better denoising results, moreover, they do not require limiting assumptions. In this article, we design an end-to-end seismic noise separation model based on the Wave-Unet, which is an adaptation of Unet for the 1-D time domain. Our designed model introduces the selective Kernel convolution into the Wave-Unet to enhance its performance and incorporates an attention-enhanced skip connection to bridge the semantic gap caused by the concatenation of features computed by networks of varying depths. Meanwhile, a joint loss function is used to enhance the model’s ability to learn signal frequency information. We tested our network using synthetic noisy signals and real seismic records from the STEAD dataset and compared the results with the wavelet threshold denoising method and the advanced omni-dimensional dynamic convolution (ODConv network) modules. The results demonstrate that our network performance surpasses the other two methods and can restore clean signals with high resolution without damaging the effective signal.