Traditional face attribute estimation algorithms have large computing power, slow reasoning speed and low accuracy, which make it difficult to integrate the algorithm into mobile or embedded devices. A multi-task face attribute estimation algorithm based on embedded system was proposed. Firstly, the bottleneck structure of MobileFaceNet network was integrated with cross stage partial network (CSPNet) and spatial pyramid pooling network (SPPNet) to design the CSPSPP_BK structure as the face attribute estimation algorithm to share the network feature extraction module. Then, in the local properties increase channel attention mechanism, using the network in a difficult global properties deeper and better performance of network model as a guide for the proposed model, the design of lightweight and multi-tasking attribute network knowledge distillation, the method of layered pruning was adopted to optimize the network model, the optimized model of quantity is only 1.8 MB. Finally, the dynamic category inhibition loss function was used to measure the loss and equalize the distribution of sample data. Compared with CelebA and Adience data sets, the average accuracy of gender and glasses is 98.89% and 99.72%, respectively. When the standard deviation is 3.01%, the accuracy of age classification is 60.21%, and the pretransmission reasoning speed on RK3288 development board is 138 fps. The results show that the proposed method can be widely applied to embedded devices and mobile edge devices.