diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.maximum.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.maximum.rst
new file mode 100644
index 00000000000..24dc3727cb7
--- /dev/null
+++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.maximum.rst
@@ -0,0 +1,6 @@
+mindspore.Tensor.maximum
+========================
+
+.. py:method:: mindspore.Tensor.maximum(other)
+
+    详情请参考 :func:`mindspore.ops.maximum`。
\ No newline at end of file
diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.ne.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.ne.rst
new file mode 100644
index 00000000000..dac4aa6e6e2
--- /dev/null
+++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.ne.rst
@@ -0,0 +1,6 @@
+mindspore.Tensor.ne
+===================
+
+.. py:method:: mindspore.Tensor.ne(other)
+
+    详情请参考 :func:`mindspore.ops.ne`。
\ No newline at end of file
diff --git a/docs/api/api_python/mindspore/mindspore.Tensor.rst b/docs/api/api_python/mindspore/mindspore.Tensor.rst
index d5559436a2a..7ed3317b7b4 100644
--- a/docs/api/api_python/mindspore/mindspore.Tensor.rst
+++ b/docs/api/api_python/mindspore/mindspore.Tensor.rst
@@ -188,6 +188,7 @@ mindspore.Tensor
     mindspore.Tensor.masked_select
     mindspore.Tensor.matrix_power
     mindspore.Tensor.max
+    mindspore.Tensor.maximum
     mindspore.Tensor.mean
     mindspore.Tensor.median
     mindspore.Tensor.mH
@@ -206,6 +207,7 @@ mindspore.Tensor
     mindspore.Tensor.nbytes
     mindspore.Tensor.ndim
     mindspore.Tensor.ndimension
+    mindspore.Tensor.ne
     mindspore.Tensor.negative
     mindspore.Tensor.nelement
     mindspore.Tensor.new_ones
diff --git a/docs/api/api_python_en/Tensor_list.rst b/docs/api/api_python_en/Tensor_list.rst
index 62aea08fe30..dc2870ac64e 100644
--- a/docs/api/api_python_en/Tensor_list.rst
+++ b/docs/api/api_python_en/Tensor_list.rst
@@ -194,6 +194,7 @@
     mindspore.Tensor.masked_select
     mindspore.Tensor.matrix_power
     mindspore.Tensor.max
+    mindspore.Tensor.maximum
     mindspore.Tensor.mean
     mindspore.Tensor.median
     mindspore.Tensor.mH
@@ -212,6 +213,7 @@
     mindspore.Tensor.nbytes
     mindspore.Tensor.ndim
     mindspore.Tensor.ndimension
+    mindspore.Tensor.ne
     mindspore.Tensor.negative
     mindspore.Tensor.nelement
     mindspore.Tensor.new_ones
diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py
index 4b298e21528..8cefc3b30a4 100644
--- a/mindspore/python/mindspore/common/tensor.py
+++ b/mindspore/python/mindspore/common/tensor.py
@@ -3941,43 +3941,7 @@ class Tensor(Tensor_):
 
     def maximum(self, other):
         r"""
-        Computes the maximum of input tensors element-wise.
-
-        Note:
-            - Inputs of `input` and `other` comply with the implicit type conversion rules to make the data
-              types consistent.
-            - The inputs must be two tensors or one tensor and one scalar.
-            - When the inputs are two tensors,
-              dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.
-            - When the inputs are one tensor and one scalar,
-              the scalar could only be a constant.
-            - Broadcasting is supported.
-            - If one of the elements being compared is a NaN, then that element is returned.
-
-        .. math::
-            output_i = max(input_i, other_i)
-
-        Args:
-            other (Union[Tensor, Number, bool]): The second input is a number or
-                a bool when the first input is a tensor or a tensor whose data type is number or bool.
-
-        Returns:
-            Tensor, the shape is the same as the one after broadcasting,
-            and the data type is the one with higher precision or higher digits among the two inputs.
-
-        Raises:
-            TypeError: If `input` and `other` is not one of the following: Tensor, Number, bool.
-            ValueError: If `input` and `other` are not the same shape.
-
-        Supported Platforms:
-            ``Ascend`` ``GPU`` ``CPU``
-
-        Examples:
-            >>> x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
-            >>> y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
-            >>> output = x.maximum(y)
-            >>> print(output)
-            [4. 5. 6.]
+        For details, please refer to :func:`mindspore.ops.maximum`.
         """
         self._init_check()
         return tensor_operator_registry.get('maximum')(self, other)
@@ -4034,35 +3998,7 @@ class Tensor(Tensor_):
 
     def ne(self, other):
         r"""
-        Computes the non-equivalence of two tensors element-wise.
-
-        Note:
-            - Input tensor and `other` comply with the implicit type conversion rules to make the data
-              types consistent.
-            - The inputs must be two tensors or one tensor and one scalar.
-            - When the inputs are two tensors, the shapes of them could be broadcast.
-            - When the inputs are one tensor and one scalar, the scalar could only be a constant.
-            - Broadcasting is supported.
-
-        Args:
-            other (Union[Tensor, Number, bool]): The second input is a number or
-                a bool when the first input is a tensor or a tensor whose data type is number or bool.
-
-        Returns:
-            Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
-
-        Raises:
-            TypeError: If input tensor and `other` is not one of the following: Tensor, Number, bool.
-            TypeError: If neither input tensor and `other` is a Tensor.
-
-        Supported Platforms:
-            ``Ascend`` ``GPU`` ``CPU``
-
-        Examples:
-            >>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
-            >>> output = x.ne(2.0)
-            >>> print(output)
-            [ True False  True]
+        For details, please refer to :func:`mindspore.ops.ne`.
         """
         self._init_check()
         return tensor_operator_registry.get('ne')(self, other)