The invention provides a low-resolution airport target detection method based on hierarchical reinforcement learning. The method comprises the steps of (1) carrying out super pixel division on an inputted remote sensing image, (2) extracting the boundary super pixel of the input image to construct a background information set, (3) learning the characteristic similarity between each super pixel and a background information set through a minimum distance similarity measurement operator and extracting a deep layer characteristic, (4) defining the ending condition of a learning process, judging whether the step (3) satisfies an ending condition or not, executing a step (6) if so, otherwise, executing a step (5), (5) using the back-propagation theory to act the deep layer characteristic in the step (3) as an reinforcement factor to a local layer input image, and taking the image which is subjected to reinforcement processing as the input image of a next layer learning process, executing the step (1), and continuing a next layer learning, (6) stopping learning, taking the deep layer characteristic learned in the local layer in the step (3) as the salient characteristic of a super pixel, and obtaining a final salient map, and (7) generating the linear feature map of an original image, fusing the linear feature map and the salient map, through salient area positioning and area combination, an airport target area is determined, and the target detection is completed.