Aiming at the problem of much noise interference in the acquired surface EMG signals and the lack of neck and shoulder muscle fatigue state classification standard and classification model, this paper proposes a neck and shoulder muscle fatigue classification method based on joint denoising and optimized limit learning machine. First, AnyBody was used to establish a biomechanical model of neck and shoulder skeletal muscles, and the muscle fatigue state was classified according to the muscle pH value and the RPE exertion perception scale. Surface EMG signals were collected from six healthy young people in the fatigue state of the upper trapezius muscle bundle. Then, joint denoising was performed by combining Kalman filtering and improved wavelet threshold function to extract six feature parameters: root mean square, integrated electromyography, mean power frequency, median frequency, instantaneous mean power frequency, and instantaneous median frequency. Finally, the weights and thresholds of Extreme Learning Machine were optimized using Improved Whale Optimization Algorithm to establish the IWOA-ELM neck and shoulder muscle fatigue classification model. The experimental results show that the joint denoising algorithm is more effective, and the accuracy of the IWOA-ELM model is 96.3% in the training set and 97.5% in the test set, with a root mean square error of 1.108, and the accuracy of the classification model is higher than 93% for different subjects, so the joint denoising algorithm and the IWOA-ELM model proposed in this paper have an advantage in classifying the fatigue of neck and shoulder muscles.