In order to identify the wear modes of the surface and study the tribological characteristics of the part surface,the surface of the two wear modes of abrasive wear and adhesive wear under different loads was studied.The wear surface topography data collection and filtering process were carried out by applying the LSTM-1 wear surface topography measuring instrument and robust Gaussian filtering method,the multifractal detrended fluctuation analysis algorithm (MF-DFA) was used to calculate the wear surface generalized Hurst exponents of the high frequency information.By analyzing the relationship between the exponents and surface topography wear texture features,the features used to identify the two wear modes were extracted by using principal component analysis.K-means clustering and support vector machine (SVM) and BP neural network methods were used to classify the extracted feature parameters,and the accuracy of the recognition results of different classifiers were compared.The research results show that the generalized Hurst exponents can be used to distinguish the texture features of furrows and pits on worn surfaces,which can be used as a machine learning feature to identify and classify surface wear modes.