diff --git a/mindspore/nn/layer/timedistributed.py b/mindspore/nn/layer/timedistributed.py
index e7ee93b05b..0b1541ef85 100644
--- a/mindspore/nn/layer/timedistributed.py
+++ b/mindspore/nn/layer/timedistributed.py
@@ -17,6 +17,7 @@
 from mindspore.ops.primitive import constexpr, Primitive
 from mindspore.ops import Reshape, Transpose, Pack, Unpack
 from mindspore.common.dtype import tensor
+from mindspore.common import Tensor
 from ..cell import Cell
 
 __all__ = ['TimeDistributed']
@@ -104,7 +105,9 @@ class TimeDistributed(Cell):
         self.reshape = Reshape()
 
     def construct(self, inputs):
-        _check_data(isinstance(inputs, tensor))
+        is_capital_tensor = isinstance(inputs, Tensor)
+        is_tensor = True if is_capital_tensor else isinstance(inputs, tensor)
+        _check_data(is_tensor)
         _check_inputs_dim(inputs.shape)
         time_axis = self.time_axis % len(inputs.shape)
         if self.reshape_with_axis is not None:
@@ -119,7 +122,9 @@ class TimeDistributed(Cell):
             inputs_shape_new = inputs.shape
             inputs = self.reshape(inputs, inputs_shape_new[: reshape_pos] + (-1,) + inputs_shape_new[reshape_pos + 2:])
             outputs = self.layer(inputs)
-            _check_data(isinstance(outputs, tensor))
+            is_capital_tensor = isinstance(outputs, Tensor)
+            is_tensor = True if is_capital_tensor else isinstance(outputs, tensor)
+            _check_data(is_tensor)
             _check_reshape_pos(reshape_pos, inputs.shape, outputs.shape)
             outputs_shape_new = outputs.shape[:reshape_pos] + inputs_shape_new[reshape_pos: reshape_pos + 2]
             if reshape_pos + 1 < len(outputs.shape):
@@ -131,7 +136,9 @@ class TimeDistributed(Cell):
         y = ()
         for item in inputs:
             outputs = self.layer(item)
-            _check_data(isinstance(outputs, tensor))
+            is_capital_tensor = isinstance(outputs, Tensor)
+            is_tensor = True if is_capital_tensor else isinstance(outputs, tensor)
+            _check_data(is_tensor)
             _check_expand_dims_axis(time_axis, outputs.ndim)
             y += (outputs,)
         y = Pack(time_axis)(y)
diff --git a/tests/st/ops/cpu/test_time_distributed_op.py b/tests/st/ops/cpu/test_time_distributed_op.py
index f6d0a3641b..000ef572a8 100644
--- a/tests/st/ops/cpu/test_time_distributed_op.py
+++ b/tests/st/ops/cpu/test_time_distributed_op.py
@@ -78,6 +78,22 @@ def test_time_distributed_dense():
     print("Dense layer wrapped successful")
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_time_distributed_dense_pynative():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
+    inputs = np.random.randint(0, 10, [32, 10])
+    dense = nn.Dense(10, 6)
+    output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy()
+    inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1)
+    time_distributed = TestTimeDistributed(dense, time_axis=1, reshape_with_axis=0)
+    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
+    for i in range(output.shape[1]):
+        assert np.all(output[:, i, :] == output_expect)
+    print("Dense layer with pynative mode wrapped successful")
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard