diff --git a/mindspore/ccsrc/pipeline/jit/parse/parse.cc b/mindspore/ccsrc/pipeline/jit/parse/parse.cc
index 9ff5381096f..5b7120a0989 100644
--- a/mindspore/ccsrc/pipeline/jit/parse/parse.cc
+++ b/mindspore/ccsrc/pipeline/jit/parse/parse.cc
@@ -806,6 +806,7 @@ FunctionBlockPtr Parser::ParseExpr(const FunctionBlockPtr &block, const py::obje
         // self.x = [xx, xx]
         // self.x.append()
         MS_LOG(DEBUG) << "The variables whose type is not parameter do not support assign operation.";
+        block->AddIsolatedNode(call_node);
       } else {
         WriteAssignVars(block, target_node, call_node);
       }
diff --git a/tests/st/syntax/test_expr_clear.py b/tests/st/syntax/test_expr_clear.py
new file mode 100644
index 00000000000..7691ee8e070
--- /dev/null
+++ b/tests/st/syntax/test_expr_clear.py
@@ -0,0 +1,56 @@
+# 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.
+# ============================================================================
+""" test graph clear statement. """
+import pytest
+import numpy as np
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import Tensor, context
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_tensorarray_clear():
+    """
+    Feature: Support clear is isolated node.
+    Description: Support clear is isolated node.
+    Expectation: No exception.
+    """
+    class Net(nn.Cell):
+        def __init__(self, dtype, element_shape):
+            super(Net, self).__init__()
+            self.ta = nn.TensorArray(dtype=dtype, element_shape=element_shape)
+            self.index_1 = 1
+            self.index_2 = 30
+
+        def construct(self, input_1, input_2):
+            size_1 = self.ta.size()
+            self.ta.write(self.index_1, input_1)
+            self.ta.write(self.index_2, input_2)
+            size_2 = self.ta.size()
+            self.ta.clear()
+            size_3 = self.ta.size()
+            return size_1, size_2, size_3
+
+    input_np_1 = np.random.randn(2, 3, 4, 5, 6).astype(np.int32)
+    input_np_2 = np.random.randn(2, 3, 4, 5, 6).astype(np.int32)
+    net = Net(dtype=ms.int32, element_shape=(2, 3, 4, 5, 6))
+    out_ms = net(Tensor(input_np_1), Tensor(input_np_2))
+    assert out_ms[0] == 0
+    assert out_ms[1] == 31
+    assert out_ms[2] == 0