Action recognition from natural scene was affected by complex illumination conditions and cluttered backgrounds. There was a growing interest in solving these problems by using 3D skeleton data. Firstly, considering the spatio-temporal features of human actions, a spatio-temporal fusion deep learning network for action recognition was proposed; Secondly, view angle invariant character was constructed based on geometric features of the skeletons. Local spatial character was extracted by short-time CNN networks. A spatio-LSTM network was used to learn the relation between joints of a skeleton frame. Temporal LSTM was used to learn spatio-temporal relation between skeleton sequences. Lastly, NTU RGB+D datasets were used to evaluate this network, the network proposed achieved the state-of-the-art performance for 3D human action analysis. Experimental results show that this network has strong robustness for view invariant sequences.