The technology of target recognition plays an important role in maritime traffic, maritime target tracking and military reconnaissance, etc. The complex ocean ambient results in the information of ship targets is incomplete, so the cloud obscured ship target will have low recognition rate and poor robustness. Therefore, this paper proposes a obscured ship target recognition algorithm based on the convolutional neural network. First, the improved InceptionV3 network is used to learn a new objective function, which is used to train the clear samples and occluded samples. Then in order to share the features of obscured samples and the clear samples, a constraint function is added to the loss function. Finally, experiments are performed on the optical remote sensing image dataset. The proposed method can improve the average recognition rates of the obscured ship target by 3.23%, 4.44%, and 15.67% than the unimproved network which are obscured 30%, 50%, 70% respectively. The experimental results show that the network model can effectively improve the low ship target recognition rate caused by cloud obscured.