The invention discloses a method for diagnosing a surface pitting fault of a blade of a wind turbine generator of an electric power system based on a machine vision image. The method comprises the first step of dividing the blade of the wind turbine generator to obtain blade elements, the second step of photographing the blade elements of the wind turbine generator and removing a background, the third step of carrying out feature extraction on image elements, the fourth step of providing a neural network clustering-LLE manifold learning algorithm to carry out dimensionality reduction on extracted multi-dimension image features, the fifth step of obtaining the normal-state image elements of the blade of the wind turbine generator and the image elements of the surface pitting fault through experiments, the sixth step of training a support vector machine used for diagnosing the fault through a training database, the seventh step of diagnosing whether the pitting fault happens to the surfaces of the blade elements of the wind turbine generator or not through the support vector machine trained in the sixth step, and the eighth step of carrying out the operations of the second step, the third step, the fourth step and the seventh step on all the blade elements until pitting diagnosis of the whole blade is completed.