［Purposes］Because there are complex textures and no structural characteristic in soil image, it is difficult to extract key features from a soil image with traditional convolutional neural network. A new classification network named visual attention ResNet (VAR), which combined ResNet with large kernel attention (LKA) module, is proposed to solve the low generalization ability that is caused by imbalance datasets and hardly classified samples and it involves VAR_tiny, VAR_small and VAR_base. ［Methods］To construct VAR network for soil classification, the LKA is embedded into ResNet as a part of the backbone and the residual modules is partially removed from ResNet, and an improved focal loss function (Focal Loss) is introduced to VAR. ［Results］1) Compared with the traditional models ResNet18, ResNet34, VGG, GooleNet, VAN, etc., VAR model has a higher identification accuracy for images data set of purple soil species under specific model parameters. 2) VAR_small, one of the three VAR models with different sizes, was compared with ResNet18 networks embedded with traditional attention mechanisms such as SE, CBAM, ECA and SK. The experimental results showed that LKA mechanism was better in soil recognition. 3) The improved Focal Loss enables VAR to pay more attention to soil image samples that are difficult to classify. ［Conclusions］The soil species identification network VAR, which integrates the LKA module and ResNet, enhances the ability of the network to extract the key structural features from soil images, makes it identify soil species more effectively. And it also reduces the network parameters.