The wide application of face recognition technology not only brings convenience to users， but also brings problems such as face spoofing and presentation attacks. Aiming at the frequent presentation attacks and print attacks， a face liveness detection algorithm based on GhostNet and feature fusion was proposed. Firstly， the feature extraction process of GhostNet model was divided into three different stages， namely， low-level feature， medium-level feature and high-level feature. Then， the feature map information of each stage was output respectively. Finally， the feature maps with different semantic information were sent into the feature fusion module for adaptive weighted fusion， so as to obtain more discriminative feature mapping. Experiments were conducted on public datasets NUAA and CelebA-Spoof. The results show that the accuracy of the proposed algorithm is 99.97% and 93.41% respectively， which is increased by 8.00 and 9.20 percentage points respectively compared with the algorithm of direct training of GhostNet model. Compared with Heterogeneous Kernel-Convolutional Neural Network （HK-CNN）， lightweight convolutional neural network FeatherNet， block based multi-stream network FaceBageNet and other algorithms， the proposed algorithm shows better performance on NUAA and CelebA-Spoof datasets. And， as GhostNet is a lightweight network model， the proposed algorithm only takes 3.6 ms on single image inference on CelebA-Spoof dataset.