鉴别野生菌的种类是预防误食有毒野生菌的重要途径。因此，为了提高野生菌细粒度识别分类的准确率，对卷积模块的注意力机制模块（CBAM）进行改进，提出了一种并行相加卷积模块的注意力机制模块PA_CBAM，将CBAM中的通道注意力模块和空间注意力模块从原来的串行连接改为并行连接并相加，解决了2种注意力模块因串行连接带来的互相干扰问题。另外，参考特征金字塔的思想改进ResNet50，其Top-1和Top-5准确率达到86.03%和97.19%，较原来提升0.86和0.73个百分点；其添加PA_CBAM后在Top-1和Top-5准确率达到88.52%、97.58%，较CBAM模块提高了3.03和0.69个百分点。此外，为了将模型移植到移动端，结合迁移学习，提出了MobileNet_v2+PA_CBAM的识别方法，准确率达到94.87%，较之前提升0.66个百分点。研究表明：提出的注意力机制模块PA_CBAM在野生菌细粒度识别研究中具有更好的识别效果，具有一定的泛用性，并且MobileNet_v2+PA_CBAM训练后模型大小仅为27.8 MB，识别图片的平均耗时仅为1.3 ms，是在移动端部署野生菌识别的理想模型。

Identifying the species of wild mushrooms is important to prevent mistaking the toxic type of mushrooms for non-toxic ones. Therefore, to improve the accuracy of the fine-grained classification of wild mushrooms, a parallel addition convolutional block attention module (PA_CBAM), which is improved from the convolutional block attention module (CBAM), is proposed. PA_CBAM changes the connections of the channel and spatial attention modules from serial to parallel and adds their results together. Consequently, the interference caused by cascading these attention modules is solved. In addition, the proposed method improves the performance of ResNet50 by referring to the concept of a feature pyramid, whose accuracies of the Top-1 and Top-5 are 86.03% and 97.19%, which are 0.86 and 0.73 percentage points higher than those of the original method, respectively. Furthermore, the Top-1 and Top-5 reach 88.52% and 97.58% using PA_CBAM, which are 3.03 and 0.69 percentage points higher, respectively. Moreover, to adapt the model for mobile terminals, combined with migration learning, the MobileNet_v2+PA_CBAM recognition method is proposed, obtaining an accuracy of 94.87%, which is 0.66 percentage points higher than that previously obtained. The results show that PA_CBAM has a better recognition and generalization effect in the fine-grained classification of wild mushrooms. Meanwhile, the size of MobileNet_v2+PA_CBAM is only 27.8 MB, and the recognition time required for a picture is only 1.3 ms, which is an ideal model for deploying wild mushrooms classification on mobile devices.