diff --git a/tests/st/ops/ascend/test_tensor_scatter_arithmetic.py b/tests/st/ops/ascend/test_tensor_scatter_arithmetic.py
new file mode 100644
index 00000000000..e669bb3198b
--- /dev/null
+++ b/tests/st/ops/ascend/test_tensor_scatter_arithmetic.py
@@ -0,0 +1,199 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import pytest
+import numpy as np
+import mindspore.nn as nn
+import mindspore.ops as ops
+import mindspore.context as context
+from mindspore.common import dtype as mstype
+from mindspore.common import Tensor, Parameter
+
+op_map = {
+    "add": ops.TensorScatterAdd,
+    "sub": ops.TensorScatterSub,
+    "max": ops.TensorScatterMax,
+    "min": ops.TensorScatterMin,
+}
+
+func_map = {
+    "add": ops.tensor_scatter_add,
+}
+
+np_func_map = {
+    "mul": lambda a, b: a * b,
+    "div": lambda a, b: a / b,
+    "add": lambda a, b: a + b,
+    "sub": lambda a, b: a - b,
+    "max": np.maximum,
+    "min": np.minimum,
+}
+
+
+class TestTensorScatterArithmeticNet(nn.Cell):
+    def __init__(self, func, input_x, indices, updates):
+        super(TestTensorScatterArithmeticNet, self).__init__()
+        self.scatter_func = op_map.get(func)()
+        self.input_x = Parameter(input_x, name="input_x")
+        self.indices = Parameter(indices, name="indices")
+        self.updates = Parameter(updates, name="updates")
+
+    def construct(self):
+        output = self.scatter_func(self.input_x, self.indices, self.updates)
+        return output
+
+
+def tensor_scatter_np(func, input_x, indices, updates):
+    result = input_x.asnumpy().copy()
+    indices_np = indices.asnumpy().copy()
+    updates_np = updates.asnumpy().copy()
+
+    f = np_func_map.get(func)
+
+    for idx, _ in np.ndenumerate(np.zeros(indices.shape[:-1])):
+        upd_idx = tuple(idx)
+        out_idx = tuple(indices_np[upd_idx])
+        result[out_idx] = f(result[out_idx], updates_np[upd_idx])
+
+    return result
+
+
+def compare_with_numpy(func, input_x, indices, updates):
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    graph_output = TestTensorScatterArithmeticNet(func, input_x, indices, updates)()
+    expected = tensor_scatter_np(func, input_x, indices, updates)
+    np.testing.assert_array_almost_equal(graph_output.asnumpy(), expected)
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    pynative_output = TestTensorScatterArithmeticNet(func, input_x, indices, updates)()
+    np.testing.assert_array_almost_equal(pynative_output.asnumpy(), expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('func', ['add', 'sub'])
+@pytest.mark.parametrize('data_type', [mstype.float16, mstype.float32])
+@pytest.mark.parametrize('index_type', [mstype.int32])
+def test_tensor_scatter_arithmetic_small_float(func, data_type, index_type):
+    """
+    Feature: TensorScatter* operators.
+    Description: test cases for TensorScatter* operator
+    Expectation: the result match numpy implementation.
+    """
+    input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), data_type)
+    indices = Tensor(np.array([[0, 0], [1, 1]]), index_type)
+    updates = Tensor(np.array([1.0, 2.2]), data_type)
+
+    compare_with_numpy(func, input_x, indices, updates)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('func', ['add', 'sub'])
+@pytest.mark.parametrize('data_type', [mstype.int8, mstype.uint8, mstype.int32, mstype.int64])
+@pytest.mark.parametrize('index_type', [mstype.int32])
+def test_tensor_scatter_arithmetic_small_int(func, data_type, index_type):
+    """
+    Feature: TensorScatter* operators.
+    Description: test cases for TensorScatter* operator
+    Expectation: the result match numpy implementation.
+    """
+    input_x = Tensor(np.array([5, 6, 7, 8, 9, 10, 11, 12]), data_type)
+    indices = Tensor(np.array([[4], [3], [1], [7]]), index_type)
+    updates = Tensor(np.array([1, 2, 3, 4]), data_type)
+
+    compare_with_numpy(func, input_x, indices, updates)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('func', ['add', 'sub'])
+@pytest.mark.parametrize('data_type', [mstype.int32, mstype.float32])
+@pytest.mark.parametrize('index_type', [mstype.int32])
+def test_tensor_scatter_arithmetic_multi_dims(func, data_type, index_type):
+    """
+    Feature: TensorScatter* operators.
+    Description: test cases for TensorScatter* operator
+    Expectation: the result match numpy implementation.
+    """
+    input_x = Tensor(np.ones((4, 4, 4)) * 10, data_type)
+    indices = Tensor(np.array([[0], [2]]), index_type)
+    updates = Tensor(
+        np.array(
+            [
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+            ]
+        ),
+        data_type,
+    )
+
+    compare_with_numpy(func, input_x, indices, updates)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('func', ['add'])
+@pytest.mark.parametrize('data_type', [mstype.float32])
+@pytest.mark.parametrize('index_type', [mstype.int32])
+def test_tensor_scatter_arithmetic_function_op(func, data_type, index_type):
+    """
+    Feature: TensorScatter* functional operators.
+    Description: test cases for ops.tensor_scatter_* api
+    Expectation: the result match numpy implementation.
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+    input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), data_type)
+    indices = Tensor(np.array([[0, 1]]), index_type)
+    updates = Tensor(np.array([1.0]), data_type)
+    expected = tensor_scatter_np(func, input_x, indices, updates)
+    output = func_map.get(func)(input_x, indices, updates)
+
+    np.testing.assert_allclose(output.asnumpy(), expected, rtol=1e-6)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('func', ['add'])
+@pytest.mark.parametrize('data_type', [mstype.float32])
+@pytest.mark.parametrize('index_type', [mstype.int32])
+def test_tensor_scatter_arithmetic_tensor_op(func, data_type, index_type):
+    """
+    Feature: TensorScatter* tensor operators.
+    Description: test cases for tensor.tensor_scatter_* api
+    Expectation: the result match numpy implementation.
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+    input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), data_type)
+    indices = Tensor(np.array([[0, 1]]), index_type)
+    updates = Tensor(np.array([1.0]), data_type)
+    expected = tensor_scatter_np(func, input_x, indices, updates)
+
+    if func == 'add':
+        output = input_x.tensor_scatter_add(indices, updates)
+
+    np.testing.assert_allclose(output.asnumpy(), expected, rtol=1e-6)
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index c3a6b0a44ba..7a1e317e73e 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -3114,6 +3114,18 @@ test_case_other_ops = [
                         Tensor(np.array([[0, 1], [1, 2]], np.int32)),
                         Tensor(np.ones([2, 5], np.float32) * 99)),
         'desc_bprop': [([3, 4, 5], {'dtype': np.float32})]}),
+    ('TensorScatterAdd', {
+        'block': P.TensorScatterAdd(),
+        'desc_inputs': (Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.float32),
+                        Tensor(np.array([[0, 1], [1, 2]], np.int32)),
+                        Tensor(np.ones([2, 5], np.float32) * 99)),
+        'desc_bprop': [([3, 4, 5], {'dtype': np.float32})]}),
+    ('TensorScatterSub', {
+        'block': P.TensorScatterSub(),
+        'desc_inputs': (Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.float32),
+                        Tensor(np.array([[0, 1], [1, 2]], np.int32)),
+                        Tensor(np.ones([2, 5], np.float32) * 99)),
+        'desc_bprop': [([3, 4, 5], {'dtype': np.float32})]}),
     ('ScatterMaxUseLocking', {
         'block': ScatterMax(use_locking=True),
         'desc_inputs': (Tensor(np.array([1, 0], np.int32)),