To address the limitations of existing CNN-based generative steganography in poor image quality and weak resistance to steganalysis, this paper proposes SSEU-Net, an improved U-Net-based steganographic architecture incorporating selective state space model, aiming to achieve high-quality image generation and secure steganography. The core contributions include: first,designing Res-SS2D module that performs quad-directional global spatial modeling on input images while maintaining linear computational complexity, thereby enhancing the visual quality of stego images; next, proposing a high-frequency feature enhancement strategy based on the observation that subtle perturbations in high-frequency regions minimally affect statistical characteristics. This strategy extracts and integrates edge features of carrier images into the encoder to guide secret information embedding into high-frequency regions, thereby reducing detectability by steganalysis; finally developing a multi-objective loss function combining PSNR and MS-SSIM for generation quality optimization, alongside introducing an L1 norm loss on low-frequency components to enforce consistency between cover and stego images in low-frequency regions, ensuring secret information is predominantly embedded in high-frequency components. Experiments demonstrate that SSEU-Net outperforms existing methods on COCO and ImageNet datasets. On ImageNet, the generated stego images achieve an average PSNR of 40.588 dB, with extracted secret images attain an average PSNR of 41.863 dB, while exhibiting strong resistance to common steganalysis.