In order to improve the accuracy of fault diagnosis for rolling bearings, this paper proposes an intelligent fault diagnosis method based on smooth and pseudo Wigner-Ville distribution (SPWVD) time-frequency distribution image texture features. First, we used the SPWVD time-frequency analysis method to process the bearing fault vibration signal. We acquired the time-frequency distribution image, then extracted the texture features from the images for the formation of the rolling bearing fault feature vectors based on certain sensitive texture features. Then, we introduced the fault feature vectors as the input to achieve fault diagnosis of rolling bearings based on the support vector machine (SVM). Finally, we extracted three kinds of feature vectors from the bearing fault data using the SPWVD time-frequency analysis method, Wigner-Ville distribution time-frequency analysis method, and wavelet scale spectrum method, respectively. We acquired the fault diagnosis accuracy of these feature vectors experimentally. The comparison results showed that the performance of the SPWVD time frequency texture fault feature outperformed the two other kinds of feature vectors with the best classification accuracy and sensitivity.