The invention discloses a method for automatically detecting the failure of an insulator. The method comprises the following steps: establishing positive and negative sample banks of insulator images, training an insulator classifier by a convolutional neural network algorithm, and splitting a collected image by utilizing the image salient area detection algorithm to split an equipment area and a background area in the image so as to obtain an insulator searching candidate area; training the insulator classifier by utilizing the convolutional neural network algorithm to locate the insulator, and controlling image collection equipment according to the location information of the insulator string in a current image to complete the image collection of the insulator string; and carrying out insulator surface stain detection, insulator string cracking detection and insulator string breakage or mixed foreign substance detection by utilizing an image identification technology so as to determine abnormal insulators. According to the method, insulator stains, cracking and breakage can be detected by adopting different image processing algorithms, insulator states can be comprehensively detected, and potential safety hazards of the insulators in transformer substations/converter stations can be reduced.