Abstract: The channel attention mechanism can effectively make use of different feature channels. By weighting and adjusting the channels of feature graphs, convolutional neural networks can pay more attention to important feature channels, thus improving their classification ability. The first step in this mechanism involves compressing the feature map of each channel to obtain global features inside each channel. Global average pooling stands out as the best choice because of its ease of use and high efficiency. However, a challenge arises when global average pooling is used to obtain the global features of channels: different channels in the feature graph have a high probability of exhibiting the same mean value. Meanwhile, using only one scalar to measure the importance of the whole feature graph will not accurately reflect the complexity and diversity of features, resulting in the lack of diversity of features after global average pooling, which further affects the classification performance of the network. To solve this problem, a split channel attention mechanism is proposed to build a module. This module extends the output dimension of global average pooling, reduces the information loss caused by global average pooling, enhances the diversity of the output features of the global average pooling layer in channel attention, and uses multiple one-dimensional convolutions to calculate the attention weight of each region in the channel dimension. By splitting the output feature map of the global average pooling layer into multiple regions, variations in the feature maps of different regions are preserved while the global information of channels is compressed. Furthermore, the importance of different regional features is considered comprehensively, and a more comprehensive and fine-grained method is adopted to evaluate and utilize feature map information than global average pooling, effectively improving the ability and performance of the model. Image classification experiments are performed on the CIFAR-100 and ImageNet datasets by combining the split channel attention mechanism with multiple image classification networks. Experimental results show that the split channel attention mechanism can effectively improve the accuracy of the model while remaining lightweight and that the proposed mechanism has better advantages than other attention mechanisms. Furthermore, Grad-CAM is used to analyze the results predicted by the model visually. The analysis results show that the network model when integrated with the split channel attention mechanism, can better learn the feature fitting of the target object region well and has better feature extraction and classification capabilities. This underscores the potential of the split attention mechanism to improve the performance of network models.