In order to improve the accuracy of arm fatigue model recognition, this study introduces time-frequency domain, nonlinearity and parametric model features based on common time-domain and frequency-domain features, and extracts 3-channel surface EMG signals to form features set. Feature dimensionality reduction is generally divided into feature extraction and feature selection. This research uses principal component analysis ( PCA) in feature extraction, kernel principal component analysis ( KPCA) and mutual information (MI) measurement methods in feature selection. Feature dimensionality reduction, using support vector machine ( SVM) and K-nearest neighbor (KNN) as the classifier; three dimensionality reduction methods and different combinations of SVM and KNN constitute a fatigue classification model. Results show that the correct recognition rate of KPCA and SVM is 99%, which is higher than other combination algorithms.