Skeleton based action recognition is playing a critical role in computer vision research, its applications have been widely deployed in many areas. Currently, benefiting from the graph convolutional networks (GCN), the performance of this task is dramatically improved due to the powerful ability of GCN for modeling the Non-Euclidean data. However, most of these works are designed for the clean skeleton data while one unavoidable drawback is such data is usually noisy in reality, since most of such data is obtained using depth camera or even estimated from RGB camera, rather than recorded by the high quality but extremely costly Motion Capture (MoCap) [1] system. Under this circumstance, we propose a novel GCN framework with adversarial training to deal with the noisy skeleton data. With the guiding of the clean data in the semantic level, a reliable graph embedding can be extracted for noisy skeleton data. Besides, a discriminator is introduced such that the feature representation could further improved since it is learned with an adversarial learning fashion. We empirically demonstrate the proposed framework based on two current largest scale skeleton-based action recognition datasets. Comparison results show the superiority of our method when compared to the state-of-the-art methods under the noisy settings.