Due to the coronavirus pandemic, the non-touch personal signature can reduce the risk of infection to a certain extent, which is of great significance to our daily life. Therefore, a simple and efficient spatiotemporal fusion network was proposed to realize skeleton-based dynamic hand gesture recognition, based on which a virtual signature system was developed. The spatiotemporal fusion network is mainly composed of spatiotemporal fusion modules based on the attention mechanism, and its key idea is to synchronously realize the extraction and fusion of spatiotemporal features using an incremental method. This network adopts different spatiotemporal coding features as inputs, and employs the double sliding window mechanism for post-processing in practical applications, thus ensuring more stable and robust results. Extensive comparative experiments on two benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art single-stream network. Besides, the virtual signature system performs well with a single normal RGB camera, which not only greatly reduces the complexity of the interaction system, but also provides a more convenient and secure approach to personal signature.