diff --git a/mindspore/nn/dynamic_lr.py b/mindspore/nn/dynamic_lr.py
index beed7a0186..3826f6a548 100644
--- a/mindspore/nn/dynamic_lr.py
+++ b/mindspore/nn/dynamic_lr.py
@@ -257,11 +257,11 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
 
     .. math::
         decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
-        (1 - tmp\_epoch / decay\_epoch)^{power} + end\_learning\_rate
+        (1 - tmp\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate
 
-    Where :math:`tmp\_epoch=min(current\_epoch, decay\_epoch), current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
-    If `update_decay_epoch` is true, update the value of `decay_epoch` every epoch. The formula is
-    :math:`decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)`
+    Where :math:`tmp\_epoch=min(current\_epoch, decay\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`, and
+    :math:`tmp\_decay\_epoch = decay\_epoch`. If `update_decay_epoch` is true, update the value of `tmp_decay_epoch`
+    every epoch. The formula is :math:`tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)`
 
     Args:
         learning_rate (float): The initial value of learning rate.
@@ -296,9 +296,10 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
     validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
     validator.check_value_type('update_decay_epoch', update_decay_epoch, [bool], None)
 
+    origin_decay_epoch = decay_epoch
     function = lambda x, y: (x, min(x, y))
     if update_decay_epoch:
-        function = lambda x, y: (x * max(math.ceil(y / x), 1), y)
+        function = lambda x, y: (origin_decay_epoch * max(math.ceil(y / origin_decay_epoch), 1), y)
 
     lr = []
     delta = learning_rate - end_learning_rate