diff --git a/docs/en/02-intro/index.md b/docs/en/02-intro/index.md
index 15f58cf5c854c2ac5ee5d5999bb2fbe07a1f7f45..9f0876d2974c625fc12ed69b117697b71ec05104 100644
--- a/docs/en/02-intro/index.md
+++ b/docs/en/02-intro/index.md
@@ -39,7 +39,7 @@ By making full use of [characteristics of time series data](https://tdengine.com
 
 - **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
 
-- **[Ease of Use]((https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to[
+- **[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to[
 ](https://tdengine.com/tdengine/easy-time-series-data-platform/) deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access. 
 
 - **[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.