diff --git a/python/paddle/autograd/autograd.py b/python/paddle/autograd/autograd.py
index c30bd56a2ce84f19ccecffe5d6c5b87e825c9b32..e61a7ff0092947cdeae1af80dae1358dbb6f1e33 100644
--- a/python/paddle/autograd/autograd.py
+++ b/python/paddle/autograd/autograd.py
@@ -553,14 +553,14 @@ def hessian(
     ``batch_axis`` means The position of the batch dimension of the parameter data.
 
     When the input ``xs`` is a Tensor tuple, the returned result is a ``Hessian`` tuple,
-    assuming that the internal shape of the ``xs`` tuple is composed of ``([M1, ], [M2, ]) ``, the shape of the returned
+    assuming that the internal shape of the ``xs`` tuple is composed of ``([M1, ], [M2, ])``, the shape of the returned
     result consists of ``(([M1, M1], [M1, M2]), ([M2, M1], [M2, M2]))``
 
     - When ``batch_axis=None``, only 0-dimensional Tensor or 1-dimensional Tensor is
       supported, assuming that the shape of ``xs`` is ``[N, ]``, and the shape of ``ys`` is ``[ ]`` (0-dimensional Tensor), the final output is a single Hessian matrix whose shape is ``[N, N]``.
 
     - When ``batch_axis=0``, only 1-dimensional Tensor or 2-dimensional Tensor is
-      supported, assuming that the shape of ``xs`` is ``[B, N]``, and the shape of ``ys`` is `` [B, ]``, the final output Jacobian matrix shape is ``[B, N, N]``.
+      supported, assuming that the shape of ``xs`` is ``[B, N]``, and the shape of ``ys`` is ``[B, ]``, the final output Jacobian matrix shape is ``[B, N, N]``.
 
     After the ``Hessian`` object is created, the complete calculation process does not
     occur, but a partial lazy evaluation method is used for calculation. It can be