In view of the high complexity of the current facial landmark detection algorithm network model, which is not conducive to deployment on devices with limited computing resources, this paper proposes a high-precision and lightweight facial landmark detection algorithm based on the idea of knowledge distillation. This algorithm improves the Bottleneck module of residual network(ResNet50) and introduces packet deconvolution to obtain a lightweight student network. At the same time, a pixel-wise loss function and a pair-wise loss function are proposed. By aligning the output feature maps and intermediate feature maps of the teacher network and the student network, the prior knowledge of the teacher network is transferred to the student network, thereby improving the detection accuracy of the student network. Experiments show that the student network obtained by this algorithm has only 2.81M parameter amount and 10.20MB model size, the frames per second on the GTX1080 graphics card is 162frames and the normalized mean error on 300W and WFLW datasets are 3.60% and 5.50%, respectively.