diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md
index 50fbc8687ef6a22486af9847c3a66f1505be3afa..11ea33c866010d2275ede0a1831a1da03e1b1320 100644
--- a/tutorials/source_en/use/data_preparation/converting_datasets.md
+++ b/tutorials/source_en/use/data_preparation/converting_datasets.md
@@ -178,12 +178,14 @@ You can use the `ImageNetToMR` class to convert the raw ImageNet data (images an
 
     Store the downloaded ImageNet dataset in a folder. The folder contains all images and a mapping file that records labels of the images.
 
-    In the mapping file, there are three columns, which are separated by spaces. They indicate image classes, label IDs, and label names. The following is an example of the mapping file:
-    ```
-    n02119789 1 pen
-    n02100735 2 notbook
-    n02110185 3 mouse
-    n02096294 4 orange
+    In the mapping file, there are two columns, which are separated by spaces. They indicate image classes and label IDs. The following is an example of the mapping file:
+    ```
+    n01440760 0
+    n01443537 1
+    n01484850 2
+    n01491361 3
+    n01494475 4
+    n01496331 5
     ```
 
 2. Import the `ImageNetToMR` class for dataset converting.
diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md
index 6a47a1a22b3b896b632aaf386421184f93306909..db0ab7d1907427964acc894ae0649ef34dec4ddb 100644
--- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md
+++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md
@@ -65,7 +65,7 @@ To read a dataset using the `MindDataset` object, perform the following steps:
     data_set = ds.MindDataset(dataset_file=CV_FILE_NAME)
     ```
     In the preceding information:  
-    `dataset_file`: specifies the MindRecord file, including the path and file name.
+    `dataset_file`: specifies the MindRecord file or list of MindRecord files.
 
 2. Create a dictionary iterator and read data records through the iterator.
     ```python
diff --git a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md
index 7bd585166ad30dc26656b09a6686fd7cacc8b398..ba744652da3054a25d64a8d55e95dfbf84da3071 100644
--- a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md
+++ b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md
@@ -178,12 +178,14 @@ MindSpore提供转换常见数据集的工具类，将常见数据集转换为Mi
 
     对下载后的ImageNet数据集，整理数据集组织形式为一个包含所有图片的文件夹，以及一个记录图片对应标签的映射文件。
 
-    标签映射文件包含3列，分别为各类别图片目录、标签ID、标签名，用空格隔开，映射文件示例如下：
-    ```
-    n02119789 1 pen
-    n02100735 2 notbook
-    n02110185 3 mouse
-    n02096294 4 orange
+    标签映射文件包含2列，分别为各类别图片目录、标签ID，用空格隔开，映射文件示例如下：
+    ```
+    n01440760 0
+    n01443537 1
+    n01484850 2
+    n01491361 3
+    n01494475 4
+    n01496331 5
     ```
 
 2. 导入转换数据集的工具类`ImageNetToMR`。
diff --git a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md
index 3186acaa2bd0c2779064fc2ae411bd324d8898f2..52a061a28df6d9838f8a7aa9b48db3901c5404e0 100644
--- a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md
+++ b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md
@@ -65,7 +65,7 @@ MindSpore天然支持读取MindSpore数据格式——`MindRecord`存储的数
     data_set = ds.MindDataset(dataset_file=CV_FILE_NAME)
     ```
     其中，  
-    `dataset_file`：指定MindRecord的文件，含路径及文件名。
+    `dataset_file`：指定MindRecord的文件或文件列表。
 
 2. 创建字典迭代器，通过迭代器读取数据记录。
     ```python