A Feature Fusion Network （FFN） was proposed to judge the quality of 3D face point cloud acquired by binocular structured light scanner. Firstly， the 3D point cloud was preprocessed to cut out the face area， and the image obtained from the point cloud and the corresponding 2D plane projection was used as the input. Secondly， Dynamic Graph Convolutional Neural Network （DGCNN） and ShuffleNet were trained for point cloud learning. Then， the middle layer features of the two network modules were extracted and fused to fine-tune the whole network. Finally， three full connected layers were used to realize the five-class classification of 3D face point cloud （excellent， ordinary， stripe， burr， deformation）. The proposed FFN achieved the classification accuracy of 83.7%， which was 5.8% higher than that of ShufflNet and 2.2% higher than that of DGCNN. The experimental results show that the weighted fusion of two-dimensional image features and point cloud features can achieve the complementary effect between different features.