diff --git a/mindspore/ccsrc/minddata/dataset/api/de_pipeline.cc b/mindspore/ccsrc/minddata/dataset/api/de_pipeline.cc
index b31fdcf63b8585ea5a24d48dfd689d890bd1ab6c..4302e12954d06f1d10a443fd337a4f31e26ed69b 100644
--- a/mindspore/ccsrc/minddata/dataset/api/de_pipeline.cc
+++ b/mindspore/ccsrc/minddata/dataset/api/de_pipeline.cc
@@ -385,9 +385,14 @@ Status DEPipeline::SaveDataset(const std::vector<std::string> &file_names, const
   }
 
   TensorRow row;
-  std::unordered_map<std::string, int32_t> column_name_id_map =
-    iterator_->GetColumnNameMap();  // map of column name, id
-  bool first_loop = true;           // build schema in first loop
+  std::unordered_map<std::string, int32_t> column_name_id_map;
+  for (auto el : iterator_->GetColumnNameMap()) {
+    std::string column_name = el.first;
+    std::transform(column_name.begin(), column_name.end(), column_name.begin(),
+                   [](unsigned char c) { return ispunct(c) ? '_' : c; });
+    column_name_id_map[column_name] = el.second;
+  }
+  bool first_loop = true;  // build schema in first loop
   do {
     json row_raw_data;
     std::map<std::string, std::unique_ptr<std::vector<uint8_t>>> row_bin_data;
@@ -402,7 +407,10 @@ Status DEPipeline::SaveDataset(const std::vector<std::string> &file_names, const
       std::vector<std::string> index_fields;
       s = FetchMetaFromTensorRow(column_name_id_map, row, &mr_json, &index_fields);
       RETURN_IF_NOT_OK(s);
-      mindrecord::ShardHeader::initialize(&mr_header, mr_json, index_fields, blob_fields, mr_schema_id);
+      if (mindrecord::SUCCESS !=
+          mindrecord::ShardHeader::initialize(&mr_header, mr_json, index_fields, blob_fields, mr_schema_id)) {
+        RETURN_STATUS_UNEXPECTED("Error: failed to initialize ShardHeader.");
+      }
       mr_writer->SetShardHeader(mr_header);
       first_loop = false;
     }
@@ -422,7 +430,9 @@ Status DEPipeline::SaveDataset(const std::vector<std::string> &file_names, const
     }
   } while (!row.empty());
   mr_writer->Commit();
-  mindrecord::ShardIndexGenerator::finalize(file_names);
+  if (mindrecord::SUCCESS != mindrecord::ShardIndexGenerator::finalize(file_names)) {
+    RETURN_STATUS_UNEXPECTED("Error: failed to finalize ShardIndexGenerator.");
+  }
   return Status::OK();
 }
 
diff --git a/tests/ut/data/mindrecord/testTFRecordData/dummy.tfrecord b/tests/ut/data/mindrecord/testTFRecordData/dummy.tfrecord
new file mode 100644
index 0000000000000000000000000000000000000000..da4f853e2d7c948921750cdf31539bed30543566
Binary files /dev/null and b/tests/ut/data/mindrecord/testTFRecordData/dummy.tfrecord differ
diff --git a/tests/ut/python/dataset/test_save_op.py b/tests/ut/python/dataset/test_save_op.py
index 2ed326276b3f04e816fcbd753ce2d3c35ae6562b..2af14aec1ce97f2fbaa16296dda41541fe44a93e 100644
--- a/tests/ut/python/dataset/test_save_op.py
+++ b/tests/ut/python/dataset/test_save_op.py
@@ -16,6 +16,7 @@
 This is the test module for saveOp.
 """
 import os
+from string import punctuation
 import mindspore.dataset as ds
 from mindspore import log as logger
 from mindspore.mindrecord import FileWriter
@@ -24,7 +25,7 @@ import pytest
 
 CV_FILE_NAME1 = "../data/mindrecord/testMindDataSet/temp.mindrecord"
 CV_FILE_NAME2 = "../data/mindrecord/testMindDataSet/auto.mindrecord"
-
+TFRECORD_FILES = "../data/mindrecord/testTFRecordData/dummy.tfrecord"
 FILES_NUM = 1
 num_readers = 1
 
@@ -388,3 +389,46 @@ def test_case_06(add_and_remove_cv_file):
 
     with pytest.raises(Exception, match="tfrecord dataset format is not supported."):
         d1.save(CV_FILE_NAME2, 1, "tfrecord")
+
+
+def cast_name(key):
+    """
+    Cast schema names which containing special characters to valid names.
+    """
+    special_symbols = set('{}{}'.format(punctuation, ' '))
+    special_symbols.remove('_')
+    new_key = ['_' if x in special_symbols else x for x in key]
+    casted_key = ''.join(new_key)
+    return casted_key
+
+
+def test_case_07():
+    if os.path.exists("{}".format(CV_FILE_NAME2)):
+        os.remove("{}".format(CV_FILE_NAME2))
+    if os.path.exists("{}.db".format(CV_FILE_NAME2)):
+        os.remove("{}.db".format(CV_FILE_NAME2))
+    d1 = ds.TFRecordDataset(TFRECORD_FILES, shuffle=False)
+    tf_data = []
+    for x in d1.create_dict_iterator():
+        tf_data.append(x)
+    d1.save(CV_FILE_NAME2, FILES_NUM)
+    d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
+                        num_parallel_workers=num_readers,
+                        shuffle=False)
+    mr_data = []
+    for x in d2.create_dict_iterator():
+        mr_data.append(x)
+    count = 0
+    for x in tf_data:
+        for k, v in x.items():
+            if isinstance(v, np.ndarray):
+                assert (v == mr_data[count][cast_name(k)]).all()
+            else:
+                assert v == mr_data[count][cast_name(k)]
+        count += 1
+    assert count == 10
+
+    if os.path.exists("{}".format(CV_FILE_NAME2)):
+        os.remove("{}".format(CV_FILE_NAME2))
+    if os.path.exists("{}.db".format(CV_FILE_NAME2)):
+        os.remove("{}.db".format(CV_FILE_NAME2))