The invention discloses an offshore oil-gas platform detection method based on a structural recurrent neural network. The method comprises: according to serialization structure characteristics of a space-time image, a structural recurrent neural network model of a seaborne target is established; parameter optimization adjustment is carried out on the established structural recurrent neural networkmodel and then a high-order space-time characteristic model of the seaborne target is established; on the basis of the high-order space-time characteristic model of the seaborne target, a high-orderspace-time characteristic of the seaborne target is obtained; a bi-directional circular convolution layer is constructed based on the high-order space-time characteristic of the seaborne target and abi-directional circular convolutional neural network with fusion of front and rear time phase features is established; and on the basis of parallel optimization adjustment of the bi-directional circular convolution layer, accurate detection of the seaborne moving target under the single time phase is realized. With the deep learning, the capability of obtaining the target feature is enhanced; andwith full utilization of the time-space features of the target, the target in the complex background interference can be detected. Moreover, the universality of the algorithm is enhanced.