Attention plays a key role in fine-grained image recognition tasks. In order to make the model pay more attention to discriminative regions, a new method based on interventional attention was proposed to provide key clues for supervising attention to learn features. Specifically, the interventional attention was added to the training process, and the attention mechanism was applied to the process of data cutting and dropping to guide the model to improve the learning efficiency. At the same time, the fused attention was applied to the feature extraction network to help the network learn more discriminable features. In addition, label smoothing loss function and center regularization loss function were introduced into the objective function, which effectively improved classification accuracy. Experimental results show that the proposed method has excellent performance, achieving 89.8%, 95.7% and 94.7% classification accuracy on CUB-200-2011, Stanford Cars and FGVC Aircraft dataset respectively. In comparison with the other mainstream fine-grained classification algorithms, the proposed method achieves better classification results.