Abstract: In order to improve the denoising accuracy and reliability of deformation monitoring data, a new denoising algorithm for deformed data is constructed based on variational mode decomposition (VMD). Firstly, the criterion for judging the high frequency noise component of VMD is established, and T index is introduced to determine the optimal K value of VMD denoising. Then, VMD component after eliminating high frequency noise is reconstructed, and the denoising method of VMD deformation data is established. Finally, the denoising methods of VMD, wavelet and empirical mode decomposition (EMD) are compared and analyzed through the examples of simulation signal, bridge deformation data and dam deformation data. The experimental results show that the correlation coefficient, root mean square error and signal-to-noise ratio of VMD are better than those of wavelet and EMD. Therefore, the validity and reliability of VMD denoising method are proved theoretically. When denoising bridge deformation data and dam deformation data, VMD denoising results have better denoising accuracy and smoothness than wavelet and EMD, while retaining the local deformation feature information.