diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 070ce68545c8a290bc6e8782ae52f9015e313bad..6b43d0d0eb36154ab84d45cd9b99c5b7e7e7a4cb 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -712,8 +712,8 @@ def merge_sliced_parameter(sliced_parameters, strategy=None):
 
     Args:
         sliced_parameters (list[Parameter]): Parameter slices in order of rank_id.
-        strategy (dict): Parameter slice strategy. Default: None.
-                         If strategy is None, just merge parameter slices in 0 axis order.
+        strategy (dict): Parameter slice strategy, the default is None.
+            If strategy is None, just merge parameter slices in 0 axis order.
 
             - key (str): Parameter name.
             - value (<class 'node_strategy_pb2.ParallelLayouts'>): Slice strategy of this parameter.
@@ -728,11 +728,15 @@ def merge_sliced_parameter(sliced_parameters, strategy=None):
 
     Examples:
         >>> strategy = build_searched_strategy("./strategy_train.ckpt")
-        >>> sliced_parameters = [\
-        Parameter(Tensor(np.array([0.00023915, 0.00013939, -0.00098059])), "network.embedding_table"), \
-        Parameter(Tensor(np.array([0.00015815, 0.00015458, -0.00012125])), "network.embedding_table"), \
-        Parameter(Tensor(np.array([0.00042165, 0.00029692, -0.00007941])), "network.embedding_tabel"), \
-        Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])), "network.embedding_table")]
+        >>> sliced_parameters = [
+        >>>                      Parameter(Tensor(np.array([0.00023915, 0.00013939, -0.00098059])),
+        >>>                                "network.embedding_table"),
+        >>>                      Parameter(Tensor(np.array([0.00015815, 0.00015458, -0.00012125])),
+        >>>                                "network.embedding_table"),
+        >>>                      Parameter(Tensor(np.array([0.00042165, 0.00029692, -0.00007941])),
+        >>>                                "network.embedding_tabel"),
+        >>>                      Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])),
+        >>>                                "network.embedding_table")]
         >>> merged_parameter = merge_sliced_parameter(sliced_parameters, strategy)
     """
     if not isinstance(sliced_parameters, list):