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.