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Convolutional neural networks, a crucial component of artificial intelligence, demonstrate outstanding performance in fields such as natural language processing and image recognition. Optimizing hyperparameters in convolutional neural network models is essential for training and optimizing large models. Existing hyperparameter optimization methods are time-consuming and may lead to local optima. In order to optimize the training process of large neural network models, a novel hyperparameter optimization method based on experimental design was proposed. Firstly, three self-built convolutional neural networks with different depths were constructed as optimization objects, aiming to find the best hyperparameter configuration to improve the model's accuracy on the validation set. Finally, in order to verify the effectiveness of the method, a training scheme was constructed based on the optimization methods, generating combinations for hyperparameter optimization and comparative experiments of subjective experience-generated training schemes were conducted. The results show that the proposed optimization method demonstrates advantages in convergence rate, accuracy, and efficiency. It is concluded that the method supports efficient training of large convolutional neural network and exhibits good generality across tasks of different scales. [ABSTRACT FROM AUTHOR]

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