As the lifeline of energy supply for various offshore platforms, submarine cables will have a huge economic and strategic impact in case of failure. Accurate prediction of submarine cable operation status will help to grasp its operation risks in advance, so as to achieve preventive maintenance. On the basis of fully mining the dynamic and static characteristics of submarine cable operation and maintenance data, a method for predicting the operation state of submarine cables based on attention mechanism and convolutional neural network gated cyclic neural network (CNN-GRU) was proposed. Firstly, considering the three key influencing factors of online monitoring, patrol inspection index and static test, the evaluation index system of submarine cable operation status was established. Then, based on the improved analytic hierarchy process (AHP) and the idea of multi-level variable weight evaluation, the evaluation model of submarine cable operation status was constructed. Finally, a combined neural network model based on attention mechanism and CNN-GRU were established, and historical operation parameters and quantitative results of status were taken as input characteristic parameters to realize the evolution trend prediction of submarine cable operation status. The analysis of numerical examples shows that the proposed method can effectively predict the operation state of submarine cables, and the average percentage error is as low as 1.04%. Compared with full connection neural network, CNN, CNN long short memory neural network (LSTM), and other algorithms, the proposed method has better prediction accuracy.