In order to find the defects of transmission lines accurately and timely, the three-dimensional inspection mode of transmission lines based on artificial intelligence image recognition technology is studied. Specifically, with the support of artificial intelligence image recognition technology, K-means algorithm is used to cluster the threedimensional inspection images. At the same time, artificial neural network is used for intelligent identification of transmission line defects in the image. According to the test, under the same workload, the transmission line defect identification without the use of artificial intelligence image recognition technology requires 5 image analysts to work continuously for 15 d, with an average of 2~3 images per minute and an image recognition speed of 20~30 s/piece; Using artificial intelligence recognition technology, recognition speed up to 0.25 s/sheet, only 3.6 h can complete the recognition task. In the low-light environment, the edge of the image is clearer after the enhanced force, and the target image is clearly separated from the background. At present, the recognition accuracy is higher than 90% by using 7 images from different angles of the same parts in the transmission line. In addition, by comparing the influence of constant learning strategies, AdaDec and AdaMix learning strategies on the convergence of the deep learning model under the same conditions, it is found that the reconstruction errors of the three strategies all show a tendency of gradually decreasing, and eventually tend to be stable. The results show that the artificial intelligence image recognition technology has a certain universality in the three-dimensional inspection image defect detection of transmission lines, and is conducive to the significant improvement of work efficiency.