Underwater bionic robots are very similar to real underwater creatures in appearance and features. This makes it difficult for autonomous underwater vehicles (AUVs) to distinguish their appearance features. Moreover, the existing underwater target detection methods have large model sizes and many parameters. This leads to difficulty in practical application of such UAV methods. To solve this, we propose an underwater camouflaged target recognition algorithm based on an adversarial graph structure. First, the salient features and environmental features of underwater images are fully explored by a custom salient feature extraction network and a related environmental feature extraction network. Second, the camouflaged target graph structure and the natural target graph structure of the fusion environment feature are constructed to obtain the dependency between the target and environment features. Finally, an adaptive frame regression camouflaged target detection network is constructed based on adversarial learning to recognize camouflaged targets and natural targets. In contrast to the existing target recognition model in the simulation of the four datasets of concealed target detection, quantitative underwater image dataset, underwater image enhancement benchmark dataset, and underwater image dataset, the proposed model gets higher average recognition accuracy of 0.820, which is an improvement of 3.4%, and the number of parameters is only 1.2 m. Proved by the experiments, the proposed target recognition algorithm is more effective and superior to the existing algorithms.