In order to reduce the influence of interference noise on gravity measured data and further improve the accuracy of gravity data processing， a gravity data denoising method based on Multilevel Wavelet Residual Network （MWRNet） was proposed， which combined wavelet transform and neural network to realize removal of noise components in gravity data. Firstly， the gravity data was decomposed by wavelet transform， and then a neural network was utilized for noise extraction， while the Residual Channel Attention （RCA） module was introduced to enhance noise extraction ability of the network. The proposed gravity data denoising method was tested using simulated data and measured data， and experimental results show that the proposed method has better results compared with other gravity data denoising algorithms. In specific， with noise level of 50， in Peak Signal-to-Noise Ratio （PSNR） and Structure SIMilarity （SSIM）， the proposed method improves over 21.8% and 9.3%， respectively， compared to the traditional denoising algorithm BM3D （Block-Matching and 3D filtering）. Compared to the deep learning-based denoising algorithms DnCNN （Denoising Convolutional Neural Network） and MWCNN （Multi-level Wavelet Convolutional Neural Network）， PSNR and SSIM are improved respectively.