The field of remote sensing target detection has experienced rapid development in recent years, demonstrating significant value in various applications. However, general detection algorithms still face many key challenges when dealing with directional target detection: firstly, conventional networks struggle to accurately represent features of rotated targets, particularly in modeling the slender shape characteristics of high-aspect-ratio targets; secondly, the mismatch between the static label allocation strategy and the feature space of dynamic rotating targets leads to bias in training sample selection under extreme-aspect-ratio scenarios. To address these issues, this paper proposes a single-stage Shape-Aware Dynamic Alignment Network (SADA-Net) that collaboratively enhances detection accuracy through feature representation optimization and adaptive label matching. The network’s design philosophy demonstrates greater flexibility and complementarity than that of previous models. Specifically, a Dynamic Refined Rotated Convolution Module (DRRCM) is designed to achieve rotation-adaptive feature alignment. An Anchor-Refined Feature Alignment Module (ARFAM) is further constructed to correct feature-to-spatial misalignment. In addition, a Shape-Aware Quality Assessment (SAQA) strategy is proposed to optimize sample matching quality based on target shape information. Experiment results demonstrate that SADA-Net achieves excellent performance comparable to state-of-the-art methods on three widely used remote sensing datasets (i.e., HRSC2016, DOTA, and UCAS-AOD).