Recently, many steganalysis approaches improve their feature extraction ability through adding convolutional layers. However, it often leads to a decrease of resolution in the feature map during downsampling, which makes it challenging to extract weak steganographic signals accurately. To address this issue, this paper proposes a multi-resolution steganalysis net (MRS-Net). MRS-Net adopts a multi-resolution network to extract global image information, fusing the output feature map to ensure high-dimensional semantic information and supplementing low-level detail information. Furthermore, the model incorporates an attention module which can analyze image sensitivity based on different channel and spatial information, thus effectively focusing on areas with rich steganographic signals. Multiple benchmark experiments on the BOSSBase 1.01 dataset demonstrate that the accuracy of MRS-Net significantly improves by 9.9% and 3.3% compared with YeNet and SRNet, respectively, demonstrating its exceptional steganalysis capability.