Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and management of cardiac conditions. However, the ECG signal is easily disrupted by various noise signals in the real world, leading to a decrease in signal quality and potentially compromising accurate clinical interpretation. With the goal of reducing noise in ECG signals, this research proposes an end-to-end multi-resolution deep learning network with attention mechanism, namely the MrSeNet to perform effective denoising of ECG data. Our MrSeNet fuses features at different scales for effective denoising with the squeeze-and-excitation module to enhance the features of the ECG signal channel. CPSC2018 database and the MIT-BIH database were used to verify the validity of the model by adding different intensity noises based on NSTDB. Using Pearson correlation coefficient, signal-to-noise ratio, and root mean square error performance evaluation model, the experimental results show that MrSeNet performs better than the traditional method, the model can achieve a good denoising effect to different degrees of noise signal data, and has a good future application prospect.