Aiming at the problems such as low efficiency, limited ability of feature extraction, and poor adaptability of traditionalclassification methods for UAV targets, this study proposes a UAV classification method that introduces attention modules to optimize deep convolutional neural networks by analyzing the characteristics of UAVs and existing classification methods. Multiple sets of comparative experiments are designed for a model structure of a convolutional neural network with three convolutional layers, three pooling layers, and two fully connected layers according to the experimental results for training to obtain the optimalclassification model for UAV targets. Then, the convolutional block attention module is introduced to strengthen and suppress feature map elements, and the batch normalization layer is introduced to accelerate convergence and improve generalization capabilities of the model. Experimental results show that after introduction of convolution block attention modules and batch normalization layers, the recognition rate of the classification model for UAV targets rises by 1.5% to 92.44%. Its advantages of high recognition rate and fast convergence over other neutral network models can basically meet the requirements of UAV target classification in actual scenes.