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A novel mechanism for skeleton-based action recognition is proposed in this paper by enhancing and fusing diverse skeleton features from distinct levels. Graph convolutional neural networks (GCNs) have been proven to be efficient in skeleton-based action recognition. However, most graph convolutional networks tend to capture and fuse discriminative information from different forms of data in spatial neighborhoods. In that case, the deeper interactions among different forms of data as well as the extraction of information in the temporal and channel dimensions are limited. To tackle the issue, we propose the ternary adaptive graph convolution (TAGC) module to capture spatiotemporal information by graph convolution. A novel skeleton information, called parallax information, is explored from original joints or bones with little computation to further improve the performance of action recognition. In addition, in order to make better use of multiple streams, multi-stream feature fusion (MSFF) is proposed to mine deeper-level hybrid features supplementing the original streams. And a graph-based ternary enhance (GTE) module is proposed to further refine the extracted discriminative features. Finally, the proposed multi-stream ternary enhanced graph convolutional network (MS-TEGCN) achieves the state-of-the-art results through extensive experiments on three challenging datasets for skeleton-based action recognition, NTU-60, NTU-120 and Kinetics-Skeleton. [ABSTRACT FROM AUTHOR]