ObjectiveThe method of using Convolutional Neural Network (CNN) for identifying disturbance signals which acquired from distributed fiber optic sensing systems has become quite common. Based on the tremendous success in image processing, Convolutional Neural Network has widely applied to the analysis and feature extraction of time-series data. In the distributed fiber optic sensing systems, disturbance signals are often caused by various factors, leading to complex and varied patterns. Traditional machine learning methods usually require experts to manually design feature extractors based on experience, which is not only time-consuming but also has potentially biased, affecting the final identification results. Compared with machine learning methods, although Convolutional Neural Network can automatically learn deep features from the data, greatly simplifying the feature extraction process, but their performance in practical applications is largely determined by the configuration of parameters. Adjusting these parameters is typically a trial-and-error process that requires substantial time and computational resources. To address this issue, this paper proposes an improved Gray Wolf Optimizer (GWO) algorithm for the automated optimization of Convolutional Neural Network parameters.MethodsGray Wolf Optimizer is a intelligent optimization algorithms that simulates the hunting behavior of gray wolves. It has the advantages of simple structure, fast convergence speed and strong global search ability. The most important aspect of the Gray Wolf Optimizer is the selection of the objective function. In this paper, the accuracy on the training set is used as the objective function, and the convolution kernel size, batch size, and output dimensions of each convolutional layer and the first fully connected layer during the training process of the neural network are taken as the parameters to be optimized. These parameters are iteratively adjusted to find the parameter combination that maximizes the objective function as much as possible.Results and DiscussionsThe training result shows that the recognition accuracy on the test set can reach 96% after the Convolutional Neural Network optimized by the improved Gray Wolf Optimizer, while the accuracy before optimization is 94%. This indicates that the improved gray wolf optimizer for parameter optimization can indeed improve the accuracy of neural network training.ConclusionsThis paper proposes a method that uses the Gray Wolf Optimizer to optimize the parameters in Convolutional Neural Network. Through the automatic optimization of Gray Wolf Optimizer, it addresses the issues of slow manual tuning and the enormous computational resources required. This method is of great significance for the parameter tuning of Convolutional Neural Network, because it introduces an intelligent optimization algorithm into Convolutional Neural Network to adjust the parameters. By further improving the Gray Wolf Optimizer or adopting other intelligent optimization algorithms, we expect to achieve higher accuracy and faster convergence speeds of the algorithms in the future.