In order to improve the accuracy of pedestrian attribute recognition, a multi-scale attention network for pedestrian attribute recognition algorithm is proposed in this paper. In order to improve the ability of feature expression and attribute recognition of the algorithm, first, the top-down feature pyramid and attention module are added to the residual network ResNet50. A top-down feature pyramid is constructed from the visual features extracted from the bottom-up. Then, the features of different scales in the feature pyramid are fused to give different weights to the channel attention of each layer of features. Finally, the model loss function is improved to weaken the impact of data imbalance on the attribute recognition rate. Experimental results on the RAP and PA-100K data sets show that compared with existing algorithms, the algorithm has better performance in terms of average accuracy, accuracy, and F1 for pedestrian attribute recognition.