This paper provides a comprehensive overview of Convolutional Neural Networks (CNNs), focusing on their history, architecture, key models, and applications. It begins with an introduction to the development of CNNs, highlighting the contributions of key researchers and the evolution from basic neural networks to modern CNNs. The paper then delves into the architecture of CNNs, explaining the role of convolution, activation functions, pooling, and full connections. It discusses various classic and advanced CNN models, such as LeNet-5, AlexNet, VGGNets, GoogLeNet, ResNet, DCGAN, MobileNets, and ShuffleNets, emphasizing their innovations and improvements over previous models. The paper also explores different types of convolutions, including one-dimensional and multi-dimensional convolutions, and their applications. It covers the use of activation functions, loss functions, and optimizers, providing experimental results to support the selection of appropriate choices. Additionally, it discusses the challenges and future directions in CNN research, including the need for lightweight models for mobile devices and the exploration of new activation functions and loss functions. Overall, the paper aims to provide a broad and in-depth understanding of CNNs, offering insights into their design, implementation, and potential applications, while also highlighting the latest advancements and open issues in the field.This paper provides a comprehensive overview of Convolutional Neural Networks (CNNs), focusing on their history, architecture, key models, and applications. It begins with an introduction to the development of CNNs, highlighting the contributions of key researchers and the evolution from basic neural networks to modern CNNs. The paper then delves into the architecture of CNNs, explaining the role of convolution, activation functions, pooling, and full connections. It discusses various classic and advanced CNN models, such as LeNet-5, AlexNet, VGGNets, GoogLeNet, ResNet, DCGAN, MobileNets, and ShuffleNets, emphasizing their innovations and improvements over previous models. The paper also explores different types of convolutions, including one-dimensional and multi-dimensional convolutions, and their applications. It covers the use of activation functions, loss functions, and optimizers, providing experimental results to support the selection of appropriate choices. Additionally, it discusses the challenges and future directions in CNN research, including the need for lightweight models for mobile devices and the exploration of new activation functions and loss functions. Overall, the paper aims to provide a broad and in-depth understanding of CNNs, offering insights into their design, implementation, and potential applications, while also highlighting the latest advancements and open issues in the field.