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Experimental researches show that for gesture recognition based on surface electromyography (sEMG) signal, the recognition accuracy will be greatly reduced due to the presence of noise. However, for the traditional denoising methods, the shortcomings could be exposed by improper decomposition of high-frequency parts or mode mixing.Aiming at the poor denoising effect caused by traditional denoising methods with improper decomposition of high-frequency parts or frequency aliasing, an algorithm of sliding interval soft threshold (SIST) denoising based on complementary ensemble empirical mode decomposition and variational mode decomposition (CEEMD-VMD-SIST) is proposed for surface electromyography (sEMG) signal. First, the noisy signal is decomposed into a group of intrinsic mode functions (IMF) from high frequency to low frequency by CEEMD, and the modal component range of the signal is objectively determined according to the autocorrelation coefficients. Then, the components are decomposed and denoised by VMD-SIST and reconstructed with some remaining modal components. Experiments show that under different signal-to-noise ratios (SNR), the denoising performance of the proposed algorithm is significantly improved compared with the traditional denoising methods, and the two evaluation indices(SNR and RMSE(root mean square error)) are significantly improved, and the useful components of signal can be retained to a greater extent.