Ship detection in complex maritime and remote sensing scenes is of great importance for Earth observation, maritime surveillance, and intelligent ocean monitoring. However, existing methods often struggle with severe background interference, adverse weather conditions, and insufficient structural feature representation of ship targets. To address these challenges, we propose VSTF-Net, a Vision-Semantic and Target-aware Fusion Framework for Ship Detection in Complex Maritime Sensing Scenarios. Specifically, to enhance high-level semantic representation under complex maritime environments, a Visual-Semantic Environment Modulation (VSEM) module is designed to introduce semantic priors extracted from a CLIP-pretrained visual encoder for adaptive feature modulation. To capture the elongated structural and directional characteristics commonly exhibited by ship targets in maritime and remote sensing scenes, a Multi-branch Asymmetric Dilated (MAD) module is developed to strengthen structural and directional feature modeling through asymmetric and dilated convolutions. Furthermore, to suppress high-frequency background interference in complex water environments, a Frequency-Adaptive Denoising (FAD) module is integrated to adaptively recalibrate frequency-domain features and improve the discriminability between ship targets and surrounding backgrounds. Experimental results show that the proposed method achieves 89.2% mAP@0.5 and 56.2% mAP@0.5:0.95 on the enhanced SeaShips dataset, with improvements of 1.9% and 2.4% over the baseline model, respectively. Further experiments on the HRSC2016 and MEIWVD datasets demonstrate the effectiveness and robustness of the proposed method across both remote sensing and maritime scenes.