diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index eedbdb6500..19828d3871 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -2222,7 +2222,7 @@ class ScatterMax(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Parameter) - The target parameter.
-        - **indices** (Tensor) - The index to do max operation whose data type should be int.
+        - **indices** (Tensor) - The index to do max operation whose data type should be mindspore.int32.
         - **updates** (Tensor) - The tensor doing the maximum operation with `input_x`,
           the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
 
@@ -2249,7 +2249,7 @@ class ScatterMax(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype, indices_dtype, updates_dtype):
-        validator.check_tensor_type_same({'indices': indices_dtype}, mstype.int_type, self.name)
+        validator.check_tensor_type_same({'indices': indices_dtype}, (mstype.int32,), self.name)
         args = {"x": x_dtype, "updates": updates_dtype}
         validator.check_tensor_type_same(args, mstype.number_type, self.name)
         return x_dtype
@@ -2266,7 +2266,7 @@ class ScatterAdd(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Parameter) - The target parameter.
-        - **indices** (Tensor) - The index to do add operation whose data type should be int.
+        - **indices** (Tensor) - The index to do add operation whose data type should be mindspore.int32.
         - **updates** (Tensor) - The tensor doing the add operation with `input_x`,
           the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
 
@@ -2292,7 +2292,7 @@ class ScatterAdd(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype, indices_dtype, updates_dtype):
-        validator.check_tensor_type_same({'indices': indices_dtype}, mstype.int_type, self.name)
+        validator.check_tensor_type_same({'indices': indices_dtype}, (mstype.int32,), self.name)
         args = {'x': x_dtype, 'updates': updates_dtype}
         validator.check_tensor_type_same(args, mstype.number_type, self.name)
         return x_dtype