diff --git a/mindspore/ops/composite/math_ops.py b/mindspore/ops/composite/math_ops.py
index 0d57c06a10e..64165d0a541 100644
--- a/mindspore/ops/composite/math_ops.py
+++ b/mindspore/ops/composite/math_ops.py
@@ -273,6 +273,13 @@ def _check_invalid_input(x1_shape, x2_shape):
                          + f'while x1 is ({len(x1_shape)}) and x2 is ({len(x2_shape)}).')
 
 
+@constexpr
+def _get_transpose_shape(x2_shape):
+    x2_shape_range = tuple(range(len(x2_shape)))
+    x2_shape_transpose = x2_shape_range[-2:-1] + x2_shape_range[:-2] + x2_shape_range[-1:]
+    return x2_shape_transpose
+
+
 def dot(x1, x2):
     """
     Computation a dot product between samples in two tensors.
@@ -304,8 +311,7 @@ def dot(x1, x2):
     _check_invalid_input(x1_shape, x2_shape)
 
     if len(x1_shape) > 2 or len(x2_shape) > 2:
-        x2_shape_range = range(len(x2_shape))
-        x2_shape_transpose = x2_shape_range[-2:-1] + x2_shape_range[:-2] + x2_shape_range[-1:]
+        x2_shape_transpose = _get_transpose_shape(x2_shape)
         x2_transpose = transpose_op(x2, x2_shape_transpose)
         x1_reshape = reshape_op(x1, (-1, x1_shape[-1]))
         x2_reshape = reshape_op(x2_transpose, (x2_shape[-2], -1))
diff --git a/tests/st/ops/cpu/test_dot_op.py b/tests/st/ops/cpu/test_dot_op.py
index a3c68889a7d..1efb5a8200c 100644
--- a/tests/st/ops/cpu/test_dot_op.py
+++ b/tests/st/ops/cpu/test_dot_op.py
@@ -207,3 +207,20 @@ def test_dot_010():
                               [[3., 3.]]]).astype(np.float32)
 
     assert (ms_result_np.asnumpy() == expect_result).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_dot_011():
+    # for document
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
+    input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
+    input_x2 = Tensor(np.array(np.ones(shape=[1, 3, 2])).astype(np.float32))
+
+    network = NetDot()
+    ms_result_np = network(input_x1, input_x2)
+    expect_result = np.array([[[3., 3.]],
+                              [[3., 3.]]]).astype(np.float32)
+
+    assert (ms_result_np.asnumpy() == expect_result).all()