diff --git a/docs/api/api_python/ops/mindspore.ops.func_soft_shrink.rst b/docs/api/api_python/ops/mindspore.ops.func_softshrink.rst
similarity index 100%
rename from docs/api/api_python/ops/mindspore.ops.func_soft_shrink.rst
rename to docs/api/api_python/ops/mindspore.ops.func_softshrink.rst
diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py
index 78c3979e9a1..42d28d21847 100644
--- a/mindspore/python/mindspore/ops/function/array_func.py
+++ b/mindspore/python/mindspore/ops/function/array_func.py
@@ -3170,12 +3170,12 @@ def gather(input_params, input_indices, axis, batch_dims=0):
     where params represents the input `input_params`, and indices represents the index to be sliced `input_indices`.
 
     .. note::
-         1. The value of input_indices must be in the range of `[0, input_param.shape[axis])`, the result is undefined
-            out of range.
+        1. The value of input_indices must be in the range of `[0, input_param.shape[axis])`, the result is undefined
+           out of range.
 
-         2. The data type of input_params cannot be
-            `bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ on Ascend
-            platform currently.
+        2. The data type of input_params cannot be
+           `bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ on Ascend
+           platform currently.
 
     Args:
         input_params (Tensor): The original Tensor. The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
diff --git a/mindspore/python/mindspore/ops/function/math_func.py b/mindspore/python/mindspore/ops/function/math_func.py
index ae1d6b5d461..c241a7044b4 100644
--- a/mindspore/python/mindspore/ops/function/math_func.py
+++ b/mindspore/python/mindspore/ops/function/math_func.py
@@ -262,7 +262,7 @@ def add(input, other):
 
     .. note::
         - Inputs of `input` and `other` comply with the implicit type conversion rules to make
-        the data types consistent.
+          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 can be broadcast.
diff --git a/mindspore/python/mindspore/ops/operations/array_ops.py b/mindspore/python/mindspore/ops/operations/array_ops.py
index 608d3691a0d..2e42e36aeb6 100755
--- a/mindspore/python/mindspore/ops/operations/array_ops.py
+++ b/mindspore/python/mindspore/ops/operations/array_ops.py
@@ -7402,7 +7402,7 @@ class SegmentMax(Primitive):
 
     Specifically, it generates a new Tensor `output` such that :math:`output_i=max_j(input\_x_j)`
     in which the maximum value is obtained from all elements corresponding
-    to :math:j that meets :math:`segment\_ids[j] == i`.
+    to :math:`j` that meets :math:`segment\_ids[j] == i`.
     If a segment contains no elements for a given segment :math:`i`,
     then the corresponding element in the output Tensor is set to zero: :math:`output[i] = 0`.
 
@@ -7455,7 +7455,7 @@ class SegmentMin(Primitive):
 
     Specifically, it generates a new Tensor `output` such that :math:`output_i=min_j(input\_x_j)`
     in which the minimum value is obtained from all elements corresponding
-    to :math:j that meets :math:`segment\_ids[j] == i`.
+    to :math:`j` that meets :math:`segment\_ids[j] == i`.
     If a segment contains no elements for a given segment :math:`i`,
     then the corresponding element in the output Tensor is set to zero: :math:`output[i] = 0`.
 
@@ -7508,7 +7508,7 @@ class SegmentSum(Primitive):
 
     Specifically, it generates a new Tensor `output` such that :math:`output_i = \sum_j input\_x_j`
     in which the cumulative sum is obtained from all elements corresponding
-    to :math:j that meets :math:`segment\_ids[j] == i`.
+    to :math:`j` that meets :math:`segment\_ids[j] == i`.
     If a segment contains no elements for a given segment :math:`i`,
     then the corresponding element in the output Tensor is set to 0: :math:`output[i] = 0`.
 
@@ -7790,7 +7790,7 @@ class SegmentMean(Primitive):
 
     Specifically, it generates a new Tensor `output` such that :math:`output_i=mean_j(input\_x_j)`
     in which the mean value is obtained from all elements corresponding
-    to :math:j that meets :math:`segment\_ids[j] == i`.
+    to :math:`j` that meets :math:`segment\_ids[j] == i`.
     If a segment contains no elements for a given segment :math:`i`,
     then the corresponding element in the output Tensor is set to zero: :math:`output[i] = 0`.
 
@@ -7847,7 +7847,7 @@ class SegmentProd(Primitive):
 
     Specifically, it generates a new Tensor `output` such that :math:`output_i = \prod_j input\_x_j`
     in which the cumulative product is obtained from all elements corresponding
-    to :math:j that meets :math:`segment\_ids[j] == i`.
+    to :math:`j` that meets :math:`segment\_ids[j] == i`.
     If a segment contains no elements for a given segment :math:`i`,
     then the corresponding element in the output Tensor is set to 1: :math:`output[i] = 1`.