The invention discloses a porcelain insulator pollution grade detection method based on color characteristics. The porcelain insulator pollution grade detection method comprises transforming a color image channel to obtain an H component, then segmenting the H component by means of a two-dimensional minimum error method combined with morphological filtering to extract an insulator disk area, extracting seven characteristic values including the mean value, the maximum value, the minimum value, the range, the variance, the grayscale anisotropy and the gray scale entropy of six channels of the disk area, and screening out characteristics which have high classification ability by means of a Fisher criterion function as pollution grade discrimination characteristics, finally taking the discrimination characteristics of the training set as an input and taking the pollution grade as an output to train a mind evolutionary algorithm (MEA) optimized BP neural network, carrying out simulation prediction by means of test set data and judging the accuracy, and achieving non-contact on-line and high-efficiency detection of the insulator pollution grade. By employing the porcelain insulator pollution grade detection method based on the color characteristics, the problem that the existing pollution grade detection method cannot achieve on-line detection is solved.