In recent years, methods based on convolutional neural networks (CNNs) have achieved significant results in the problem of target classification of synthetic aperture radar (SAR) images. However, the challenges of SAR image data labeling and the characteristics of CNNs relying on a large amount of labeled data for training have seriously limited the further development of this field. In this work, we propose an approach based on attention mechanism and feature complementary fusion (AFCF-CNN) to address these challenges. First, we design and construct a feature complementary module for extracting and fusing multi-layer features, making full use of limited data and utilizing contextual information between different layers to capture more robust feature representations. Then, the attention mechanism reduces the interference of redundant background information, while it highlights the weight information of key targets in the image to further enhance the key local feature representations. Finally, experiments conducted on the moving and stationary target acquisition and recognition dataset show that our model significantly outperforms other state-of-the-art methods despite severe shortages of training data.