diff --git a/doc/fluid/new_docs/beginners_guide/quick_start/fit_a_line/README.cn.md b/doc/fluid/new_docs/beginners_guide/quick_start/fit_a_line/README.cn.md
index 605ed44f6e212ddff8afa0a20540e42f417bce31..41a14ad5801b84cad6babd4b38a2b11992edd5ee 100644
--- a/doc/fluid/new_docs/beginners_guide/quick_start/fit_a_line/README.cn.md
+++ b/doc/fluid/new_docs/beginners_guide/quick_start/fit_a_line/README.cn.md
@@ -52,22 +52,88 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
 ### 数据集介绍
 这份数据集共506行，每行包含了波士顿郊区的一类房屋的相关信息及该类房屋价格的中位数。其各维属性的意义如下：
 
-| 属性名 | 解释 | 类型 |
-| ------| ------ | ------ |
-| CRIM | 该镇的人均犯罪率 | 连续值 |
-| ZN | 占地面积超过25,000平方呎的住宅用地比例 | 连续值 |
-| INDUS | 非零售商业用地比例 | 连续值 |
-| CHAS | 是否邻近 Charles River  | 离散值，1=邻近；0=不邻近 |
-| NOX | 一氧化氮浓度 | 连续值 |
-| RM | 每栋房屋的平均客房数 | 连续值 |
-| AGE | 1940年之前建成的自用单位比例 | 连续值 |
-| DIS | 到波士顿5个就业中心的加权距离 | 连续值 |
-| RAD | 到径向公路的可达性指数 | 连续值 |
-| TAX | 全值财产税率 | 连续值 |
-| PTRATIO | 学生与教师的比例 | 连续值 |
-| B | 1000(BK - 0.63)^2，其中BK为黑人占比 | 连续值 |
-| LSTAT | 低收入人群占比 | 连续值 |
-| MEDV | 同类房屋价格的中位数 | 连续值 |
+<p align="center">
+<table>
+    <thead>
+    <tr>
+        <th>Attribute Name</th>
+        <th>Characteristic</th>
+        <th>Data Type</th>
+    </tr>
+    </thead>
+    <tbody>
+    <tr>
+        <td>CRIM</td>
+        <td>per capita crime rate by town</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>ZN</td>
+        <td>proportion of residential land zoned for lots over 25,000 sq.ft.</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>INDUS</td>
+        <td>proportion of non-retail business acres per town</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>CHAS</td>
+        <td>Charles River dummy variable</td>
+        <td>Discrete, 1 if tract bounds river; 0 otherwise</td>
+    </tr>
+    <tr>
+        <td>NOX</td>
+        <td>nitric oxides concentration (parts per 10 million)</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>RM</td>
+        <td>average number of rooms per dwelling</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>AGE</td>
+        <td>proportion of owner-occupied units built prior to 1940</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>DIS</td>
+        <td>weighted distances to five Boston employment centres</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>RAD</td>
+        <td>index of accessibility to radial highways</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>TAX</td>
+        <td>full-value property-tax rate per $10,000</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>PTRATIO</td>
+        <td>pupil-teacher ratio by town</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>B</td>
+        <td>1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>LSTAT</td>
+        <td>% lower status of the population</td>
+        <td>Continuous</td>
+    </tr>
+    <tr>
+        <td>MEDV</td>
+        <td>Median value of owner-occupied homes in $1000's</td>
+        <td>Continuous</td>
+    </tr></tbody>
+</table>
+</p>
 
 ### 数据预处理
 #### 连续值与离散值
diff --git a/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md b/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
index 21c51c7d61f34f1a6e899eb1d699cc028c1ff207..136c739b20261e19a375bd4c8b849b1413e3631f 100644
--- a/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
+++ b/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
@@ -107,12 +107,38 @@ Softmax回归模型采用了最简单的两层神经网络，即只有输入层
 
 PaddlePaddle在API中提供了自动加载[MNIST](http://yann.lecun.com/exdb/mnist/)数据的模块`paddle.dataset.mnist`。加载后的数据位于`/home/username/.cache/paddle/dataset/mnist`下：
 
-| 文件名称                | 说明                       |
-|-------------------------|----------------------------|
-| train-images-idx3-ubyte | 训练数据图片，60,000条数据 |
-| train-labels-idx1-ubyte | 训练数据标签，60,000条数据 |
-| t10k-images-idx3-ubyte  | 测试数据图片，10,000条数据 |
-| t10k-labels-idx1-ubyte  | 测试数据标签，10,000条数据 |
+<p align="center">
+<table>
+    <thead>
+    <tr>
+        <th>File name</th>
+        <th>Description</th>
+        <th>Size</th>
+    </tr>
+    </thead>
+    <tbody>
+    <tr>
+        <td>train-images-idx3-ubyte</td>
+        <td>Training images</td>
+        <td>60,000</td>
+    </tr>
+    <tr>
+        <td>train-labels-idx1-ubyte</td>
+        <td>Training labels</td>
+        <td>60,000</td>
+    </tr>
+    <tr>
+        <td>t10k-images-idx3-ubyte</td>
+        <td>Evaluation images</td>
+        <td>10,000</td>
+    </tr>
+    <tr>
+        <td>t10k-labels-idx1-ubyte</td>
+        <td>Evaluation labels</td>
+        <td>10,000</td>
+    </tr></tbody>
+</table>
+</p>
 
 ## Fluid API 概述