Aiming at the difficulty of rolling bearing fault feature extraction in the encoder instantaneous angular speed (IAS) signal, a parameter-adaptive SMHD rolling bearing IAS signal feature extraction method is proposed by combining the advantages of the sparse maximum harmonics-to-noise-ratio deconvolution ( SMHD) algorithm, which can extract the periodic impulse fault component in the signal without a priori period. Firstly, the IAS signal is estimated using the forward difference method. Then, the fault characteristics (FC) are utilized as an adaptive criterion for selecting the optimal length of the SMHD filter, achieving adaptive determination of the filter length. Subsequently, the optimized filter length is applied to enhance the IAS signal using the SMHD algorithm. Finally, the fault characteristics of the rolling bearing are revealed through envelope analysis. The effectiveness of the proposed method is validated through analysis of both simulated and measured data.