For conquering the problems of traditional fault diagnosis algorithm such as low diagnosis efficiency, low accuracy and the generalization of model is not strong enough, a fault diagnosis algorithm based on Principal Component Analysis feature PCA optimizing, fuzzy k-means and K-Nearest Neighbor clustering is proposed. Firstly, the main model is introduced and diagnosis principle is designed. Then the PCA method is used to realize the feature optimizing and the data after dimension reducing is obtained, so as to improve the diagnosis efficiency. The training sample data is used to train the fuzzy k-means classifier and get the cluster distance and threshold is computed.to get which the classifier it owns to, then the KNN algorithm is used to obtain its K nearest neighbors to compute the sum of distance square between it and them, by comparing the relationship of value of sum of distance square with distance sum threshold it can conclude whether the sample is fault sample or normal sample. The fault diagnosis experiment of antifriction bearing experiment is operated, and the result shows it has the high convergence rapid, diagnosis efficiency and diagnosis accuracy, so the method in this paper can be accepted as a universal fault diagnosis method and has large application foresight.