To recognize aircraft targets in airport remote sensing images quickly and accurately, a recognition algorithm combining the deep convolutional neural network with the edge contour feature extraction technique is proposed. The depth features of the aircrafts in the airport remote sensing image are extracted by using the deep convolutional neural network. To solve the shadow problem in aircraft parking positions, the target contour is obtained by using the optimized Canny operator, and then the aircrafts are classified by using Support Vector Machine (SVM). The the algorithm consists of the following two stages. The first stage is the training phase, which mainly trains the deep convolutional neural network and normalizes the obtained features. Then the edge features are obtained by using Canny operator and the major axis is obtained by using Principal Component Analysis (PCA) method. The Euclidean distance between the edge points along the two sides of the spindle is extracted as the eigenvector, and finally SVM classifier training is implemented. The second stage is the testing phase, in which the algorithm is verified and its accuracy is tested. Experimental results show that the recognition rate of the method can reach 94. 39%, which can effectively recognize the aircraft targets.