The invention discloses an image retrieval method based on a hierarchical convolutional neural network, and mainly aims at solving the problem that in existing all-sky aurora image retrieval, the accurate rate is low. The method comprises the implementation steps that 1, local key points of all-sky aurora images are determined by adopting an adaptive polar barrier method; 2, local SIFT features ofthe all-sky aurora images are extracted, and a visual vocabulary is constructed; 3, the convolutional neural network is pre-trained and subjected to fine tuning, and a polar region pooling layer is constructed; 4, region CNN features and global CNN features of the all-sky aurora images are extracted; 5, all the features are subjected to binarization processing, and hierarchical features are constructed; 6, a reverse index table is constructed, and the global CNN features are saved separately; and 7, hierarchical features of a queried image are extracted, the similarity between the queried image and the database images is calculated, and a retrieval result is output. According to the method, matching of the local key points is achieved through the hierarchical features, the problem that inan existing image retrieval method, the false alarm rate is high is solved, the advantage of being high in retrieval accuracy rate is achieved, and the method is suitable for real-time image retrieval.