Complicated underwater environments, such as occlusion by foreign objects and dim light, cause serious loss of underwater object features. This certainly increases the challenge of accurate detection of underwater objects. To tackle the above problems, we proposed an underwater occlusion object recognition algorithm combined with significant environmental features. First, the salient features of the underwater object were extracted by the salient feature extraction network. Second, salient environmental features associated with the object were extracted by the environmental feature attention mechanism. Finally, the contrast graph structure of the object’s salient features and related environmental features was constructed. The model proposed is compared with the existing object recognition model in the simulation of the three datasets: the underwater image enhancement benchmark dataset, video diver dataset, and underwater image dataset. The average recognition accuracy of the proposed model is 80.07%, which is improved by 2.38%. Experiments show that the object recognition algorithm proposed is effective and superior to existing algorithms.