Surface Electromyography (sEMG) signal is a kind of weak physiological signal that effectively represent muscle activities; however, it is susceptible to many noise interferences in the acquisition process. In order to adaptively set key parameters of Variational Mode Decomposition (VMD) and further eliminate the noises in the sEMG signal, a sEMG signal denoising method based on Improved Sparrow Search Algorithm (ISSA) optimized VMD and second-generation wavelets threshold is proposed in this paper. Firstly, The VMD parameters setting was optimized by adopting ISSA based on improved tent chaotic mapping, adaptive weight and dynamic change of the population number of sparrows, and quality factors were used as objective function. The optimized VMD was used to decompose the pre-treated sEMG signal, and the signal and noise components were distinguished by the spectrum correlation analysis. Finally, the signal component was denoised by the second-generation wavelet threshold to obtain the denoising signal. The results are shown that: ISSA can effectively improve parameter optimization ability for VMD compared with SSA, the denoising method for sEMG signal based on ISSA-VMD and second-generation wavelet hard threshold has better denoising performance than other methods under different noise levels. For actual sEMG signals, the method based on ISSA-VMD and the second-generation wavelet hard threshold can effectively remove noise.