diff --git a/docs/api/api_python/mindspore.boost.rst b/docs/api/api_python/mindspore.boost.rst
index b3e4690dc9..ab9496ceeb 100644
--- a/docs/api/api_python/mindspore.boost.rst
+++ b/docs/api/api_python/mindspore.boost.rst
@@ -113,7 +113,7 @@ Boost能够自动加速网络，如减少BN/梯度冻结/累积梯度等。
 
         其中：
 
-        - pca_mat (array)： 维度(k*n)，k是 *n_components*的大小，n是权重的大小。
+        - pca_mat (array)： 维度(k*n)，k是 *n_components* 的大小，n是权重的大小。
         - bk (array)： 维度(k*k)，bk是拟牛顿法中的对称正定矩阵。
 
         我们需要找到满足以下条件的m：
@@ -121,7 +121,7 @@ Boost能够自动加速网络，如减少BN/梯度冻结/累积梯度等。
         .. math::
             new\_loss < old\_loss + delta\_loss
 
-        然后使用 *delta_grad*去更新模型的权重：
+        然后使用 *delta_grad* 去更新模型的权重：
 
         .. math::
 
diff --git a/docs/api/api_python/ops/mindspore.ops.UniformCandidateSampler.rst b/docs/api/api_python/ops/mindspore.ops.UniformCandidateSampler.rst
index f22a65b212..0e2a73952c 100644
--- a/docs/api/api_python/ops/mindspore.ops.UniformCandidateSampler.rst
+++ b/docs/api/api_python/ops/mindspore.ops.UniformCandidateSampler.rst
@@ -10,7 +10,7 @@
     **参数：**
 
     - **num_true** (int) - 每个训练样本的目标类数。
-    - **num_sampled** (int) - 随机采样的类数。sampled_candidates的shape将为 `num_sampled` 。如果`unique` 为True，则 `num_sampled` 必须小于或等于 `range_max` 。
+    - **num_sampled** (int) - 随机采样的类数。sampled_candidates的shape将为 `num_sampled` 。如果 `unique` 为True，则 `num_sampled` 必须小于或等于 `range_max` 。
     - **unique** (bool) - 表示一个batch中的所有采样类是否唯一。
     - **range_max** (int) - 可能的类数，该值必须是非负的。
     - **seed** (int) - 随机种子，该值必须是非负的。如果seed的值为0，则seed的值将被随机生成的值替换。默认值：0。
diff --git a/mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py b/mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py
index 98f17b995c..6245bb2609 100644
--- a/mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py
+++ b/mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py
@@ -75,7 +75,7 @@ class TFRecordToMR:
         source (str): TFRecord file to be transformed.
         destination (str): MindRecord file path to transform into, ensure that no file with the same name
             exists in the directory.
-        feature_dict (dict[str, `FixedLenFeature <https://www.tensorflow.org/api_docs/python/tf/io/FixedLenFeature>`_]): Dictionary
+        feature_dict (dict[str, `FixedLenFeature <https://www.tensorflow.org/api_docs/python/tf/io/FixedLenFeature>`_ ]): Dictionary
             that states the feature type, and `VarLenFeature <https://www.tensorflow.org/api_docs/python/tf/io/VarLenFeature>`_
             is not supported.
         bytes_fields (list[str], optional): The bytes fields which are in `feature_dict` and can be images bytes.
diff --git a/mindspore/python/mindspore/nn/optim/thor.py b/mindspore/python/mindspore/nn/optim/thor.py
index 47747321c6..8616ffcb38 100644
--- a/mindspore/python/mindspore/nn/optim/thor.py
+++ b/mindspore/python/mindspore/nn/optim/thor.py
@@ -273,7 +273,7 @@ def thor(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0
     :math:`\lambda` represents :math:`damping`, :math:`g_i` represents gradients of the i-th layer,
     :math:`\otimes` represents Kronecker product, :math:`\gamma` represents 'learning rate'
 
-     Note:
+    Note:
         When a parameter group is separated, 'weight_decay' of each group is applied to the corresponding parameter.
         'weight_decay' in the optimizer is applied to arguments that do not have 'beta' or 'gamma' in their name
         when the argument group is not separated.
diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py
index 8be1cde27a..b79ef659b8 100644
--- a/mindspore/python/mindspore/ops/function/array_func.py
+++ b/mindspore/python/mindspore/ops/function/array_func.py
@@ -46,7 +46,7 @@ def unique(x):
           The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
 
     Returns:
-        Tuple, containing Tensor objects `(y, idx), `y` is a tensor with the
+        Tuple, containing Tensor objects (`y`, `idx`), `y` is a tensor with the
         same type as `x`, and contains the unique elements in `x`.
         `idx` is a tensor containing indices of elements in
         the input corresponding to the output tensor, have the same shape with `x`.