## 1 Introduction  
The convolutional neural network (CNN) has attracted considerable attention because of its successful application in target detection, image classification, knowledge acquisition, and image semantic segmentation. Consequently, improving its performance is a research hotspot [1]. When solving the problem of image target detection, the central processing unit (CPU) controls the entire process and the data scheduling of the CNN, and the graphics processing unit (GPU) improves the convolution calculation in the neural network unit and the operation speed of the full-joint layer-merging cell [2]. Although the learning speed of the neural network has been improved, data conversion and scheduling between the CPU and the GPU lead to an increase in time-cost, and a weak GPU platform is prone to process interruption. In [3], an improved CNN based on the immune mechanism was obtained. Although a short recognition time of 108.501 s was obtained on the MNIST dataset, the recognition rate was merely 81.6%. In [4], the optimal network structure algorithm for the weighted value sharing of the multiple-input sigmoid activation function neural network of a feature mapping model was obtained. In [5], different-sized neuron kernels were used to form a feature graph by introducing neuron elements into the largest collection layer, thus mapping neurons of different sizes to the collection layer. Although the method obtained a 96.33% recognition rate in the feature character set, it consumed considerable time. The convolutional neural network with fully connected Multilayer perceptron (MLP) (MLP-CNN) [6] improves the performance of the model by increasing the number of neural network features and uses a random gradient descent algorithm to optimize cross-entropy. Studies have been conducted to mitigate overfitting. In [7], an adjoint objective function was designed; additionally, an auxiliary listening mechanism and a normalized auxiliary listening strategy, based on the convolution filter and nonlinear activation function, were established. Then, a regularization strategy mechanism of the adjoint objective function was proposed to reduce this problem in CNNs. However, this method must use end-to-end supervised learning to fine-tune the regularization strategy, which requires additional time for the convolution filter and the nonlinear activation function. In [8], the network parameters were optimized using the Laplace-Beltrami operator, and good results were obtained in solving the weighted magnetic resonance image recognition problem. However, the overfitting problem caused by the small sample data needed to use supervised learning to fine-tune the training network. In [9], the CNN unsupervised learning function was used to introduce a bilinear interpolation into the CNN structure and the fine-grained aesthetic quality for predicting classification to achieve the automatic aesthetic evaluation of the photographs. Although the model addressed the incapability of the CNN in fully extracting the entire picture features of the high-quality photographs, a corresponding solution to the overfitting problem caused by the small digital photographic picture set was not proposed. In [10], a modified convolutional neural network was obtained by embedding a compute layer for local averaging and a quadratic feature extraction in each convolution layer, and its unique two-time feature extraction structure reduced the feature resolution. However, when the dataset scale was enlarged, the layer number of the CNN increased, which easily led to overfitting. The weighted CNN (WCNN) [11] uses the sigmoid function as the activation function to achieve the processing of the input signal by compounding multiple convolutions and pool layers. Meanwhile, the mapping relationship between the connecting layer and the output target is established, and the clustering algorithm is used to classify the features.