Aiming at the characteristics of non-stationary, nonlinear, and susceptible to noise interference in distributed fiber optic sensing signals, as well as the problem of low recognition rate of submarine cable status by a single sensor, a multi-sensor fusion based method for identifying the exposed status of submarine cables is proposed. Firstly, the optical fiber sensing signal is processed using optimized variational mode decomposition (VMD), and the intrinsic mode function (IMF) is selected using the correlation coefficient method; Secondly, the IMF components selected by multiple sensors are sequentially arranged and encoded into grayscale images; Finally, design a deep convolutional neural network (DCNN) structure, input the training set into the network for training, and validate the effectiveness of the network with the test set to achieve recognition of the exposed state of submarine cables. By using on-site collected temperature and vibration data of submarine cables, the testing accuracy reached 99.90%, and the results showed that this method can accurately identify the exposed state of submarine cables; The testing accuracy of adding gaussian noise to the original signal reaches 99.75%, proving that this method has good generalization ability and anti-noise performance.