In order to realize the accurate detection and classification of muscle fatigue states, a new complete muscle fatigue detection and classification system based on human surface ElectroMyoGraphy (sEMG) signals was proposed. Firstly, human sEMG signals were collected through AgCl surface patch electrode and high-precision analog front-end device ADS1299. The time-domain and frequency-domain features of sEMG signals reflecting human muscle fatigue states were extracted after the denoising preprocessing using wavelet transformation. Then, on the basis of the common features such as Integrated ElectroMyoGraphy (IEMG), Root Mean Square (RMS), Median Frequency (MF), Mean Power Frequency (MPF), in order to depict the fatigue states of human muscle more finely, the Band Spectral Entropy (BSE) of frequency domain features of sEMG signals were introduced. In order to compensate the weakness of Fourier transform in dealing with non-stationary signals, the time-frequency feature of the sEMG signals, named mean instantaneous frequency based on Ensemble Empirical Mode Decomposition-Hilbert transform (EEMD-HT), was introduced. Finally, in order to improve the classification accuracy of muscle non-fatigue and fatigue states, the Support Vector Machine optimized by Particle Swarm Optimization algorithm (PSO-SVM) with mutation was used for the classification of sEMG signals to realize the detection of human muscle fatigue states. Fifteen healthy young men were recruited to carry out sEMG signal acquisition experiments, and a sEMG signal database was established to extract features for classification. The experimental results show that, the proposed system can realize high-accuracy sEMG signal acquisition and high-accuracy classification of muscle fatigue states, and its accuracy rate of classification is above 90%.