Recently, pedestrian attribute recognition has been extensively studied that has benefited from the vigorous development of deep learning. However, it is difficult to capture the fine-grained attributes in the image due to complex and diversified attributes, poor image quality, and viewing angle occlusion, which is very challenging. Based on deep learning, we propose a multi-scale residual attention network(MRAN) for pedestrian attribute recognition with Resnet50 as the main architecture, using lightweight pyramid convolution to provide parallel convolution with different kernel sizes to complete multi-scale information extraction. The attention module is embedded to focus on the key areas where the attributes exist and explore the internal relations of the attributes. Secondly, the feature pyramid aggregation strategy is used to more fully extract and fuse multi-scale features. The network combines the ideas of multi-scale learning, attention mechanism and residual learning to enable the network to extract richer and more delicate features. Finally, an experimental study is carried out on the two datasets of PETA and PA100K, and the results show that the proposed method is superior to the existing research methods. Through ablation research, the effectiveness and advancement of the three components of the entire network architecture are verified, and the proposed network has bidirectional optimization with high accuracy and low complexity.