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The safety and reliability of GIS equipment are of great significance for the smooth operation of the power system. Therefore, in order to improve the detection effect of GIS equipment defects and improve the safety of equipment operation, a non-destructive testing method for GIS equipment defects is proposed based on X-ray digital imaging. Collect GIS equipment images through X-ray digital imaging and denoise the Poisson noise present in the images to improve image quality. For the processed image, two-dimensional principal component analysis is used to represent the original data by converting complex image data into simple principal components, and extract the most representative features. Input the extracted results into a BP neural network classifier and perform non-destructive testing of GIS equipment defects through feature classification. The experimental results show that after applying this method, the image recognition clarity is high and the detection effect for different types of defects is good. The advantage of this method lies in the use of advanced image processing and machine learning techniques, which can effectively identify and locate defects in GIS equipment. By timely discovering and repairing these defects, the safety and reliability of GIS equipment can be improved, thereby ensuring the smooth operation of the power system.