Aiming at the problem that the weld area of the power station cooling pipe ray image has low contrast, the features are not obvious, and the traditional method is difficult to achieve an accurate search, a method for searching the power station cooling water pipe weld seam area based on deep learning is proposed.Contrast-limited adaptive histogram equalization(CLAHE) is used to limit the amplitude of the statistical histogram of the image and suppress the amplification of noise to obtain the cumulative distribution function(CDF) of the histogram to correct the low contrast of the image.The 24 convolutional layers of the deep neural network are used to extract the features of the input image, and the 2 fully connected layers predict the image position and class probability to achieve the detection of the welded seam area of the water-cooled wall tube and overcome the problems of the traditional template with low accuracy and high time complexity.The 100 refrigeration tube ray pictures were divided into training set, validation set and test set according to 4∶1∶5.The training set and validation set are used to train the deep neural network, and the position of the welding pipe area of the refrigeration pipe is predicted using the trained model.The experimental results show that the method of searching seam area based on deep learning can realize the precise search of the weld seam with an accuracy rate of 96% and high search efficiency and accuracy.