Due to the nonlinearity and non-smoothness of surface electromyography ( sEMG) signals, which leads to the limitations of traditional muscle fatigue classification methods, a muscle fatigue classification method based on the combination of Fourier decomposition method (FDM) and machine learning is proposed based on this. The FDM is used to decompose the sEMG signal into a series of Fourier intrinsic band functions (FIBF), determine the optimal decomposition level, extract the ratio of the total power of each FIBF component to the total power of the sEMG signal (FTPR) as classification features using the FDM, and compare the effectiveness of each machine learning classification algorithm and the effect of data length on the classification accuracy. It was shown that the FDMbased feature extraction method can effectively identify muscle fatigue states, and an average classification accuracy of 98. 17% was obtained using a support vector machine classifier with a data length of 3 000 and a 10-level decomposition level of FDM. Each FIBF component was analyzed individually, and it was found that the FTPR under the 5th FIBF component had the best class separability, and the FTPR of the 1st to 2nd components would become larger when muscle fatigue was present, and the FTPR of the 4th to 10th components would become smaller, i. e. , the frequency amplitude of the sEMG signal in the 0 ~ 117 Hz interval would increase when muscle fatigue was present, and the frequency amplitude in the 175. 5 ~ 585 Hz interval would decreases. By comparing the muscle fatigue classification effects of different feature extraction methods, the experimental results show that the FDM and FTPR features can significantly improve the classification accuracy. Therefore, the proposed method can be used for muscle fatigue state recognition.