diff --git a/docs/api/api_python/mindspore.ops.rst b/docs/api/api_python/mindspore.ops.rst
index 71dc8f55db6..ad100f5eed2 100644
--- a/docs/api/api_python/mindspore.ops.rst
+++ b/docs/api/api_python/mindspore.ops.rst
@@ -65,7 +65,7 @@ mindspore.ops
     mindspore.ops.margin_ranking_loss
     mindspore.ops.mse_loss
     mindspore.ops.multi_margin_loss
-    mindspore.ops.multi_label_margin_loss
+    mindspore.ops.multilabel_margin_loss
     mindspore.ops.multilabel_soft_margin_loss
     mindspore.ops.nll_loss
     mindspore.ops.smooth_l1_loss
diff --git a/docs/api/api_python/ops/mindspore.ops.MultilabelMarginLoss.rst b/docs/api/api_python/ops/mindspore.ops.MultilabelMarginLoss.rst
index 00d83330218..97c0af60fac 100644
--- a/docs/api/api_python/ops/mindspore.ops.MultilabelMarginLoss.rst
+++ b/docs/api/api_python/ops/mindspore.ops.MultilabelMarginLoss.rst
@@ -8,4 +8,4 @@ mindspore.ops.MultilabelMarginLoss
     创建一个标准，用于优化输入 :math:`x` （一个2D小批量Tensor）
     和输出 :math:`y` （一个目标类别索引的2DTensor）之间的多类分类铰链损失（基于边距的损失）。
 
-    更多细节请参考 :func:`mindspore.ops.multi_label_margin_loss` 。
+    更多细节请参考 :func:`mindspore.ops.multilabel_margin_loss` 。
diff --git a/docs/api/api_python/ops/mindspore.ops.func_multi_label_margin_loss.rst b/docs/api/api_python/ops/mindspore.ops.func_multi_label_margin_loss.rst
index 3ecd8827fa4..a6f8493a467 100644
--- a/docs/api/api_python/ops/mindspore.ops.func_multi_label_margin_loss.rst
+++ b/docs/api/api_python/ops/mindspore.ops.func_multi_label_margin_loss.rst
@@ -1,7 +1,7 @@
-mindspore.ops.multi_label_margin_loss
+mindspore.ops.multilabel_margin_loss
 ======================================
 
-.. py:function:: mindspore.ops.multi_label_margin_loss(inputs, target, reduction='mean')
+.. py:function:: mindspore.ops.multilabel_margin_loss(inputs, target, reduction='mean')
 
     用于优化多标签分类问题的铰链损失。
 
diff --git a/docs/api/api_python_en/mindspore.ops.rst b/docs/api/api_python_en/mindspore.ops.rst
index 28ecdc38be0..b2dfcda5f2f 100644
--- a/docs/api/api_python_en/mindspore.ops.rst
+++ b/docs/api/api_python_en/mindspore.ops.rst
@@ -65,7 +65,7 @@ Loss Functions
     mindspore.ops.margin_ranking_loss
     mindspore.ops.mse_loss
     mindspore.ops.multi_margin_loss
-    mindspore.ops.multi_label_margin_loss
+    mindspore.ops.multilabel_margin_loss
     mindspore.ops.multilabel_soft_margin_loss
     mindspore.ops.nll_loss
     mindspore.ops.smooth_l1_loss
diff --git a/mindspore/python/mindspore/ops/function/__init__.py b/mindspore/python/mindspore/ops/function/__init__.py
index 2a0612bc341..c0780585149 100644
--- a/mindspore/python/mindspore/ops/function/__init__.py
+++ b/mindspore/python/mindspore/ops/function/__init__.py
@@ -468,7 +468,7 @@ from .nn_func import (
     conv3d,
     glu,
     multi_margin_loss,
-    multi_label_margin_loss,
+    multilabel_margin_loss,
     multilabel_soft_margin_loss,
     elu,
     gelu,
diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py
index d251dad8153..dac94456e00 100644
--- a/mindspore/python/mindspore/ops/function/nn_func.py
+++ b/mindspore/python/mindspore/ops/function/nn_func.py
@@ -5197,7 +5197,7 @@ def multi_margin_loss(inputs, target, p=1, margin=1, weight=None, reduction='mea
     return outputs
 
 
-def multi_label_margin_loss(inputs, target, reduction='mean'):
+def multilabel_margin_loss(inputs, target, reduction='mean'):
     r"""
     Hinge loss for optimizing a multi-label classification.
 
@@ -5248,10 +5248,9 @@ def multi_label_margin_loss(inputs, target, reduction='mean'):
     Examples:
        >>> inputs = Tensor(np.array([[0.1, 0.2, 0.4, 0.8], [0.2, 0.3, 0.5, 0.7]]), mindspore.float32)
        >>> target = Tensor(np.array([[1, 2, 0, 3], [2, 3, -1, 1]]), mindspore.int32)
-       >>> output, _ = ops.multi_label_margin_loss(inputs, target)
+       >>> output = ops.multilabel_margin_loss(inputs, target)
        >>> print(output)
-       (Tensor(shape=[], dtype=Float32, value= 0.325), Tensor(shape=[2, 4], dtype=Int32, value=
-       [[1, 1, 1, 1], [0, 0, 1, 1]]))
+       0.325
     """
 
     loss = _get_cache_prim(P.MultilabelMarginLoss)(reduction)
@@ -5851,7 +5850,7 @@ __all__ = [
     'glu',
     'margin_ranking_loss',
     'multi_margin_loss',
-    'multi_label_margin_loss',
+    'multilabel_margin_loss',
     'multilabel_soft_margin_loss',
     'elu',
     'gelu',
diff --git a/mindspore/python/mindspore/ops/operations/nn_ops.py b/mindspore/python/mindspore/ops/operations/nn_ops.py
index 8764d7218ef..7a13e9f5f07 100644
--- a/mindspore/python/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/python/mindspore/ops/operations/nn_ops.py
@@ -8808,7 +8808,7 @@ class MultilabelMarginLoss(Primitive):
     hinge loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`)
     and output :math:`y` (which is a 2D `Tensor` of target class indices).
 
-    Refer to :func:`mindspore.ops.multi_label_margin_loss` for more details.
+    Refer to :func:`mindspore.ops.multilabel_margin_loss` for more details.
 
     Supported Platforms:
         ``Ascend`` ``GPU``