Abstract: Aiming at the problem of time-consuming, laborious and costly classification of traditional body parts and body types, this paper designs a body type classification network (A_BCN) that incorporates attention mechanism fine-grained classification. The network consisted of two modules: weakly supervised attention learning and data enhancement. The weakly supervised attention learning module obtained the attention map of the human body through the attention mechanism, and the data enhancement module guided the data enhancement of the image through the attention map, including attention cropping, attention dropping and attention averaging. The enhanced image was re-input into the network to get the feature map, and the feature map and attention map were fused for classification. In the subsequent self-made human body image data set, the accuracy of the algorithm is 90.52%, which improves the classification accuracy and saves the cost.