diff --git a/mindspore/ccsrc/minddata/mindrecord/meta/shard_column.cc b/mindspore/ccsrc/minddata/mindrecord/meta/shard_column.cc
index eb4229be9c..a34634fd1d 100644
--- a/mindspore/ccsrc/minddata/mindrecord/meta/shard_column.cc
+++ b/mindspore/ccsrc/minddata/mindrecord/meta/shard_column.cc
@@ -137,6 +137,10 @@ MSRStatus ShardColumn::GetColumnFromJson(const std::string &column_name, const j
   // Initialize num bytes
   *n_bytes = ColumnDataTypeSize[column_data_type];
   auto json_column_value = columns_json[column_name];
+  if (!json_column_value.is_string() && !json_column_value.is_number()) {
+    MS_LOG(ERROR) << "Conversion failed (" << json_column_value << ").";
+    return FAILED;
+  }
   switch (column_data_type) {
     case ColumnFloat32: {
       return GetFloat<float>(data_ptr, json_column_value, false);
@@ -152,7 +156,12 @@ MSRStatus ShardColumn::GetColumnFromJson(const std::string &column_name, const j
     }
     default: {
       // Convert string to c_str
-      std::string tmp_string = json_column_value;
+      std::string tmp_string;
+      if (json_column_value.is_string()) {
+        tmp_string = json_column_value.get<string>();
+      } else {
+        tmp_string = json_column_value.dump();
+      }
       *n_bytes = tmp_string.size();
       auto data = reinterpret_cast<const unsigned char *>(common::SafeCStr(tmp_string));
       *data_ptr = std::make_unique<unsigned char[]>(*n_bytes);
@@ -169,10 +178,6 @@ template <typename T>
 MSRStatus ShardColumn::GetFloat(std::unique_ptr<unsigned char[]> *data_ptr, const json &json_column_value,
                                 bool use_double) {
   std::unique_ptr<T[]> array_data = std::make_unique<T[]>(1);
-  if (!json_column_value.is_string() && !json_column_value.is_number()) {
-    MS_LOG(ERROR) << "Conversion to float failed (" << json_column_value << ").";
-    return FAILED;
-  }
   if (json_column_value.is_number()) {
     array_data[0] = json_column_value;
   } else {
diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 58ff041d35..a8a2bb785e 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -1147,7 +1147,8 @@ class Dataset:
             1. To save the samples in order, set dataset's shuffle to False and num_files to 1.
             2. Before calling the function, do not use batch operator, repeat operator or data augmentation operators
                with random attribute in map operator.
-            3. Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) nor
+            3. Can not save number type tensor whose shape is dynamic.
+            4. Mindrecord does not support DE_UINT64, multi-dimensional DE_UINT8(drop dimension) nor
                multi-dimensional DE_STRING.
 
         Args:
diff --git a/tests/ut/python/dataset/test_minddataset_padded.py b/tests/ut/python/dataset/test_minddataset_padded.py
index b87e050054..97daba392a 100644
--- a/tests/ut/python/dataset/test_minddataset_padded.py
+++ b/tests/ut/python/dataset/test_minddataset_padded.py
@@ -136,6 +136,33 @@ def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
     assert num_padded_iter == 5
     assert num_iter == 15
 
+def test_cv_minddataset_reader_basic_padded_samples_type_cast(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["label", "file_name", "data"]
+
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['label'] = -1
+    padded_sample['file_name'] = 99999
+    num_readers = 4
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, padded_sample=padded_sample, num_padded=5)
+    assert data_set.get_dataset_size() == 15
+    num_iter = 0
+    num_padded_iter = 0
+    for item in data_set.create_dict_iterator(num_epochs=1, output_numpy=True):
+        logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
+        if item['label'] == -1:
+            num_padded_iter += 1
+            assert item['file_name'] == bytes(str(padded_sample['file_name']),
+                                              encoding='utf8')
+            assert item['label'] == padded_sample['label']
+            assert (item['data'] == np.array(list(padded_sample['data']))).all()
+        num_iter += 1
+    assert num_padded_iter == 5
+    assert num_iter == 15
+
 
 def test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file):
     """tutorial for cv minddataset."""