diff --git a/ctr/README.cn.md b/ctr/README.cn.md
index 1b2a73757575404dfd7790440bf9f27617084c1d..a4cb6d17144a9d78a2764e8a49d3abaeb918b7b6 100644
--- a/ctr/README.cn.md
+++ b/ctr/README.cn.md
@@ -146,8 +146,8 @@ Wide & Deep Learning Model\[[3](#参考文献)\] 可以作为一种相对成熟
 Figure 2. Wide & Deep Model
 </p>
 
-模型左边的 Wide 部分，可以容纳大规模系数特征，并且对一些特定的信息（比如 ID）有一定的记忆能力；
-而模型右边的 Deep 部分，能够学习特征间的隐含关系，在相同数量的特征下有更好的学习和推导能力。
+模型上边的 Wide 部分，可以容纳大规模系数特征，并且对一些特定的信息（比如 ID）有一定的记忆能力；
+而模型下边的 Deep 部分，能够学习特征间的隐含关系，在相同数量的特征下有更好的学习和推导能力。
 
 
 ### 编写模型输入
diff --git a/ctr/README.md b/ctr/README.md
index 391c842edb4e4d16badd23a4f3c2545dbb16df89..6f11ac60734b4a549a9c84d7fbba8ed283a97284 100644
--- a/ctr/README.md
+++ b/ctr/README.md
@@ -120,7 +120,7 @@ The model structure is as follows:
 Figure 2. Wide & Deep Model
 </p>
 
-The wide part of the left side of the model can accommodate large-scale coefficient features and has some memory for some specific information (such as ID); and the Deep part of the right side of the model can learn the implicit relationship between features.
+The wide part of the top side of the model can accommodate large-scale coefficient features and has some memory for some specific information (such as ID); and the Deep part of the bottom side of the model can learn the implicit relationship between features.
 
 
 ### Model Input
diff --git a/ctr/images/wide_deep.png b/ctr/images/wide_deep.png
index 03c4afcfc6cea0b5abf4c4554ecf9810843e75e2..616f88cb22607c1c6bcbe4312644f632ef284e8e 100644
Binary files a/ctr/images/wide_deep.png and b/ctr/images/wide_deep.png differ