diff --git a/python/paddle/metric/metrics.py b/python/paddle/metric/metrics.py
index 4d28b68f994560d40187bfeb1e739a7d767bb674..919daa31d06fc47505ca26940b6979fb31fc2652 100644
--- a/python/paddle/metric/metrics.py
+++ b/python/paddle/metric/metrics.py
@@ -776,7 +776,7 @@ def accuracy(input, label, k=1, correct=None, total=None, name=None):
     Args:
         input(Tensor): The input of accuracy layer, which is the predictions of network. A Tensor with type float32,float64.
             The shape is ``[sample_number, class_dim]`` .
-        label(Tensor): The label of dataset. Tensor with type int64. The shape is ``[sample_number, 1]`` .
+        label(Tensor): The label of dataset. Tensor with type int64 or int32. The shape is ``[sample_number, 1]`` .
         k(int, optional): The top k predictions for each class will be checked. Data type is int64 or int32.
         correct(Tensor, optional): The correct predictions count. A Tensor with type int64 or int32.
         total(Tensor, optional): The total entries count. A tensor with type int64 or int32.
@@ -796,6 +796,8 @@ def accuracy(input, label, k=1, correct=None, total=None, name=None):
             result = paddle.metric.accuracy(input=predictions, label=label, k=1)
             # [0.5]
     """
+    if label.dtype == paddle.int32:
+        label = paddle.cast(label, paddle.int64)
     if _non_static_mode():
         if correct is None:
             correct = _varbase_creator(dtype="int32")