Owing to the limited availability of samples and unbalanced categories of bone images， it is difficult to classify these images. To improve the classification accuracy of bone images， this study developed a bone-image classification method based on auxiliary classifier generative adversarial network （ACGAN） data generation and transfer learning. First， an multi-attention U-Net-based ACGAN （MU-ACGAN） model was designed to address the imbalance of bone-image categories. The model uses U-Net as the generator framework and combines dense residual connection and channel-spatial attention mechanism to improve the generation of bone-image detail features. The discriminator extracts bone-image features by using a dense residual attention convolution block for discrimination. Next， the amount of data was further expanded via combination with traditional data enhancement methods. Finally， a multi-scale convolutional neural network was designed to extract the features at different scales of bone imaging so as to improve the classification effect. In the model training process， a two-stage transfer learning method was adopted to optimize the initialization parameters of the model and address the problem of overfitting. Experimental results indicate that the classification accuracy of the proposed method reaches 85.71%， effectively alleviating the problem of low classification accuracy on small sample bone-image datasets.