In the field of military aerial object recognition， due to the lack of samples， current artificial intelligence algorithms cannot perform well. This paper uses the existing sufficient auxiliary domain images to assist the application domain with few samples for cross-domain object recognition and solves the problem of weak generalization ability and poor performance of the recognition model caused by missing labels and sparse samples. A cross-domain object recognition algorithm named Deep-Shallow Learning Graph Model （D-SLGM） is proposed. Firstly， a deep-shallow two-stream feature extraction algorithm is proposed to solve the problem of feature representation under unsupervised few-shot conditions. At the same time， a feature fusion algorithm based on graph model is proposed to realize high precision fusion between features. Then， a recognition model is trained based on the fused features， the generalization ability of the algorithm is improved. The self-built aerial object dataset is adopted with three application scenarios. The experimental results show that the mean average recognition accuracy of D-SLGM reaches 78.2%， which is better than those of the comparison methods. D-SLGM has great potential in actual aerial object recognition applications.