diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index b819a136bbf..e4be9f56584 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1532,7 +1532,8 @@ class ParallelConcat(PrimitiveWithInfer):
         The input tensors are all required to have size 1 in the first dimension.
 
     Inputs:
-        - **values** (tuple, list) - Tuple or list of input tensors.
+        - **values** (tuple, list) - Tuple or list of input tensors. The data type and shape of these
+          tensors must be same.
 
     Outputs:
         Tensor, data type same as `values`.
@@ -1542,6 +1543,7 @@ class ParallelConcat(PrimitiveWithInfer):
         >>> data2 = Tensor(np.array([[2, 1]]).astype(np.int32))
         >>> op = P.ParallelConcat()
         >>> output = op((data1, data2))
+        [[0, 1], [2, 1]]
     """
 
     @prim_attr_register
@@ -1553,14 +1555,15 @@ class ParallelConcat(PrimitiveWithInfer):
         x_type = values['dtype']
 
         validator.check_integer(f'x_shp length', len(x_shp), 1, Rel.GE, self.name)
+
+        args = {f"x_type[{i}]": elem for i, elem in enumerate(x_type)}
+        validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.name)
+
         first_elem = x_shp[0]
-        args = {}
         for i, elem in enumerate(x_shp[1:]):
             j = i + 1
-            args[f'x_type[{j}]'] = x_type[j]
             validator.check_integer(f'x_shp[{j}][0]', elem[0], 1, Rel.EQ, self.name)
             validator.check(f"x_shp[0] shape", first_elem, f"x_shp[{j}] shape", elem, Rel.EQ, self.name)
-        validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.name)
 
         ret_shp = x_shp[0].copy()
         ret_shp[0] = len(x_shp)