diff --git a/doc/fluid/design/async_executor/async_executor.md b/doc/fluid/design/async_executor/async_executor.md
index 725ebc4e7f30f180ea9062f7015371cbd0149fa4..b59745a2ba0d172e4037325a91c426fb8c8c5e16 100644
--- a/doc/fluid/design/async_executor/async_executor.md
+++ b/doc/fluid/design/async_executor/async_executor.md
@@ -49,7 +49,7 @@ async_executor is mainly designed for cpu training scenarios where data throughp
 executor and parallel_executor are designed for geneneral training cases in particular for gpu training. Executor is a single thread implementation for model training and it is mostly used for startup_program running currently. Another application scenario of executor is reinforcement learning where input data and main_program may change through training. Parallel_executor is mainly designed for synchronous training on high performance devices such as gpu. Operators are executed concurrently following topological orders on different graphs and model parameter gradients are synchrounized iteratively.
 
 ## Data Feeding Approach
-![Data Feeding Approach](https://github.com/guru4elephant/FluidDoc/blob/develop/doc/fluid/design/async_executor/reader_mechanisms.png)
+![Data Feeding Approach](https://github.com/guru4elephant/FluidDoc/blob/develop/doc/fluid/design/async_executor/async_executor_reader_design.png)
 to be discussed. 
 
 ## Main Interface of Async Executor