Detecting and recognizing targets on the ground is one of the typical tasks of UAVs (unmanned aerial vehicles), but it is limited by the task particularity so that it is often difficult to obtain sufficient data about target samples to achieve highly reliable target recognition. In view of this problem, a small-sample vehicle target recognition method based on the component model was proposed, which combined the cognitive characteristics of human beings to improve the perception ability of ground targets. The possible region of the target was extracted by visual saliency detection and objectness detection, and then the GrabCut segmentation method based on the Graph theory and the maximum between-class variance was used to segment the target and to extract the components from the target. A component recognition method based on a probability map model was used to perform component recognition by sparsely representing a component outline as a conditional random field and performing probabilistic reasoning. The Bayesian-based target recognition method was used to determine whether the target was a vehicle. Verification on real images captured by the UAV showed that the algorithm can detect and identify the vehicle target with high accuracy under the condition of fewer samples, poorer illumination and certain occlusion. At the same time, the recognition method can achieve the effect of certain interpretability.