The detection of nearshore wave period is crucial for fine nearshore wave forecast. Thus, we propose a novel method to realize automatic detection of nearshore wave period by learning spatiotemporal features from nearshore wave surveillance videos. The method takes continuous ocean wave video frames as inputs. First, a two-dimensional convolutional neural network (2D-CNN) is used to extract spatial features of the video frame images, and the extracted spatial features are spliced into sequences in the time dimension. Then a one-dimensional convolutional neural network (1D-CNN) is used to extract temporal features. The composite convolutional neural network (CNN-2D1D) can realize the effective fusion of wave space-time information. Finally, the attention mechanism is used to adjust the weight of the fusion features and linearly maps the fusion features to wave period. The method in this paper is compared with the detection method only extracting spatial features based on VGG16 network and the detection method for spatiotemporal feature fusion based on the ConvLSTM and three-dimensional convolutional (C3D) network. The results of experiments show that C3D and CNN-2D1D achieve the best detection results, with an average absolute error of 0.47 s and 0.48 s, respectively, but CNN-2D1D is more stable than C3D, with a lower root-mean-square error (0.66) than C3D (0.81). And CNN-2D1D requires fewer training parameters. These results show that the proposed method is more effective in wave period detection.