In order to solve the problems of low locating precision and low recognition rate of the airport identification algorithm in sub-meter high-resolution remote sensing imagesa new identification algorithm based on Deep Convolutional Neural Network (DCNN) is proposed.Firstlythe bi-cubic interpolation algorithm is used to down-sample the original single-phase remote sensing images and convert them into grayscale imagesand the pre-processed images are obtained by fuzzy enhancement.Secondlythe edge information of the images is detected by using Canny edge detection operatorand the straight line segments are extracted by using probability Hough transform.The linear regions are preliminarily screened and merged by judging whether there are parallel lines.ThenDCNN is used for judging the merged regions to acquire the recognition probability of the corresponding regions.Finallythe airport area is obtained by analyzing the probability values of the candidate regions.Simulation experiments were made to the two kinds of remote sensing images with high resolutionthe recognition rate was 100% and the mean locating accuracy was 87.53%which proved the validity and versatility of the proposed algorithm.