diff --git a/README.md b/README.md
index 61cfbab48096d8f2fc6edac48ad2d0986f2ea7b3..682c2202ce43d6e61464621316cfc342df8431f8 100644
--- a/README.md
+++ b/README.md
@@ -85,7 +85,7 @@ In most cases of large-scale graph learning, we need distributed graph storage a
 The following are 13 graph learning models that have been implemented in the framework.
 
 |Model | feature |
-|---|---|--- |
+|---|---|
 | GCN | Graph Convolutional Neural Networks |
 | GAT | Graph Attention Network |
 | GraphSage |Large-scale graph convolution network based on neighborhood sampling|
diff --git a/docs/source/md/introduction.md b/docs/source/md/introduction.md
index 1392b2275667f03af9125c8fa67c1efd5072e67a..ec7a4bfe60604b5e7984843eb0c660e7ba391ede 100644
--- a/docs/source/md/introduction.md
+++ b/docs/source/md/introduction.md
@@ -103,7 +103,7 @@ In most cases of large-scale graph learning, we need distributed graph storage a
 The following are 13 graph learning models that have been implemented in the framework.
 
 |Model | feature |
-|---|---|--- |
+|---|---|
 | [GCN](examples/gcn_examples.html)| Graph Convolutional Neural Networks |
 | [GAT](examples/gat_examples.html)| Graph Attention Network |
 | [GraphSage](examples/graphsage_examples.html)|Large-scale graph convolution network based on neighborhood sampling|