Recent years have witnessed significant progress in the field of few-shot image classification while few-shot 3D point cloud classification still remains under-explored. Real-world 3D point cloud data often suffers from occlusions, noise and deformation, which make the few-shot 3D point cloud classification even more challenging. In this paper, we propose a cross-modality feature fusion network, for few-shot 3D point cloud classification, which aims to recognize an object given only a few labeled samples, and provides better performance even with point cloud data with missing points. More specifically, we train two models in parallel. One is a projection-based model with ResNet18 as the backbone and the other one is a point-based model with a DGCNN backbone. Moreover, we design a Support-Query Mutual Attention (sqMA) module to fully exploit the correlation between support and query features. Extensive experiments on three datasets, namely ModelNet40, ModelNet40-C and ScanObjectNN, show the effectiveness of our method, and its robustness to missing points. Our proposed method outperforms different state-of-the-art baselines on all datasets. The margin of improvement is even larger on the ScanObjectNN dataset, which is collected from real-world scenes and is more challenging with objects having missing points.