Skeleton data is compact and robust to environmental conditions for hand gesture recognition. Recent studies of skeleton-based hand gesture recognition often use deep neural networks to extract spatial and temporal information. However, these methods are likely to have problems such as complicated computation and a large number of model parameters. To solve this problem, this study presents a lightweight and efficient hand gesture recognition model. It uses two spatial geometric features calculated from skeleton sequences and automatically learned motion trajectory features to achieve hand gesture classification with convolutional networks alone as its backbone network. The proposed model has a minimum number of parameters as small as 0.16M and a maximum computational complexity of 0.03 GFLOPs. This method is also evaluated on two public datasets, where it outperforms the other methods that use skeleton modality as input.