Aiming at the problems that the watermarking method based on deep learning does not fully highlight key features of the image and does not utilize the output features of the intermediate convolution layer effectively， to improve the visual quality and the ability to resist noise attacks of the watermarked image， an attention mechanism-based multi-scale feature image watermarking method was proposed. An attention module was designed in the encoder part to focus on important image features， thereby reducing image distortion caused by watermark embedding； a multi-scale feature extraction module was designed in the decoder part to capture different levels of image details. Experimental results show that compared with the deep watermark model HiDDeN（Hiding Data with Deep Networks） on COCO dataset， the proposed method has the generated watermarked image’s Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） increased by 11.63% and 1.29% respectively and has the average Bit Error Rate （BER） of watermark extraction for dropout， cropout， crop， Gaussian blur， and JPEG compression reduced by 53.85%. In addition， ablation experimental results confirm that the method adding attention module and multi-scale feature extraction module has better invisibility and robustness.