Range–Doppler (RD) spectrogram-based maritime target recognition requires effective feature learning when labeled training samples are limited and the range and Doppler axes convey different physical information. We propose DAMD-Net, which combines Asymmetric Dimension-Aware Fusion (ADF) with a Dual-Directional Attention Module (DDAM). ADF integrates range-wise scattering features, Doppler-wise motion features, and local two-dimensional context, while DDAM recalibrates the fused representation through axis-specific descriptors and learnable fusion weights. With 5% of the simulated dataset used for training, DAMD-Net achieves $86.15\pm 0.43$% accuracy and $83.57\pm 0.41$% macro-F1. With 10% used for training, the corresponding results are $89.55\pm 0.39$% accuracy and $87.48\pm 0.51$% macro-F1. At the smaller 1% and 2% training budgets, DAMD-Net improves macro-F1 over VGG16 by 1.64 and 1.82 percentage points, respectively, with positive unadjusted 95% paired confidence intervals. Each training budget uses an additional 4049 labeled validation samples. Component ablations, kernel and pooling sensitivity studies, and condition-holdout evaluations examine the contribution of directional modeling; DAMD-Net has higher mean accuracy than VGG16 in all three elevation-holdout intervals. Controlled perturbation tests show advantages under partial Doppler masking and structured synthetic clutter: at a baseline-RD-to-added-clutter ratio of $-10$ dB, accuracy reaches 62.74%, compared with 36.68% for VGG16. On the external MTDSP dataset, DAMD-Net achieves 97.13% accuracy; on the measured LSS-DAUR dataset, it achieves $75.00\pm 14.03$% accuracy under five-fold cross-validation. DAMD-Net also reduces parameters by 89.9% and registered FLOPs by 52.6% relative to VGG16. Together, these findings support explicit RD anisotropy modeling for recognition with restricted training data, with complementary benefits in the evaluated disturbance conditions and model storage.