Based on the research background of the CNC spindle bearing, this paper proposes a fault diagnosis method combining wavelet packet mixing feature and support vector machine（SVM）, aiming at the problem that bearing fault information is complex and difficult to obtain and fault data samples are few. First, carry out wavelet packet decomposition and reconstruction of the bearing vibration signal, and extract the mixed features of the signal to construct a joint feature space. Then use tdistributed stochastic neighbor embedding (tSNE) method to observe the distribution of sample data and observe the data distribution of the mixed feature sample set. Finally, a nonlinear SVM is used for fault classification. The Experimental results show that the accuracy is 100% for the fault identification of the spindle bearing inner ring, outer ring and ball. Compared with the fault classification effect of linear SVM and BP neural network, this method has achieved good results in the application of fault diagnosis of spindle bearing of CNC.