Due to the uneven distribution of wild mushrooms in China, the diversity of species, and the difficulty for the general population to distinguish whether wild mushrooms are edible, there occur positioning accidents from time to time and thus it is urgent to propose an efficient method to identify wild mushroom species. Here, a new type of wild mushroom specie image recognition model (Dis-Xception-CBAM) is proposed, which takes the Xception structure as a benchmark, combines the transfer learning method with network weights for feature learning, adds the attention mechanism to further extract the explicit features of wild mushrooms, and introduces the feature map distortion structure to increase the model's generalization performance. A dataset of 33 kinds of wild mushroom images is constructed based on the common wild mushrooms in China and the corresponding experiments are conducted. When the initial learning rate is 0.001 and the number of training iterations is 300, the Top 1 reaches 96.32% and the Top 5 reaches 99.61%. Compared with the traditional image recognition model, the proposed model obtains a better result, which provides a theoretical basis for wild mushroom recognition research.