In order to improve the classification ability and diagnostic accuracy of turbine vibration signals, a new feature extraction method from fault signals of turbine vibration based on the manifold learning method (MLM) is proposed. The results show that the MLM effectively extracts fault feature information of turbine vibration and separates different types of fault feature information. The diagnostic accuracy of features extracted by the MLM is significantly higher than that of the wavelet packet analysis method.