n this paper, the convolutional neural network is applied to the identification of gravitational wave signals, the influence of the maximum pooling layer parameters on the classification ability of the model is studied, and the accuracy of the superparameters in the model to improve the classification of gravitational wave signals is adjusted. The optimized network structure and Gabbard′s convolutional neural network were used for the same simulation data set, and the Receiver Operating Characteristic curve (ROC curve) was plotted on the test set and the ROC curve area was calculated.The results show that our model has increased the area under ROC by 0.0254 to 0.0326 compared with the unoptimized network. At the same time, we also changed the amplitude of the noise and applied the two methods to the new data set. The results also prove that the network is better and more robust after optimization.