diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index 1f5786f911cb..6a23c8da9739 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -1923,6 +1923,16 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _softsign():
+    # op description: https://www.tensorflow.org/api_docs/python/tf/math/softsign
+    def _impl(inputs, attr, params, mod):
+        abs_out = get_relay_op("abs")(inputs[0])
+        add_out = abs_out + tvm.relay.const(1, attr["T"].name)
+        return inputs[0] / add_out
+
+    return _impl
+
+
 def _softplus():
     # op description: https://www.tensorflow.org/api_docs/python/tf/math/softplus
     def _impl(inputs, attr, params, mod):
@@ -2381,6 +2391,7 @@ def _impl(inputs, attr, params, mod):
     "Slice": _slice(),
     "Softmax": _softmax(),
     "Softplus": _softplus(),
+    "Softsign": _softsign(),
     "SpaceToBatchND": _space_to_batch_nd(),
     "SpaceToDepth": _space_to_depth(),
     "Split": _split(False),
diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
index 713993ea40a4..df6682e28a9d 100644
--- a/tests/python/frontend/tensorflow/test_forward.py
+++ b/tests/python/frontend/tensorflow/test_forward.py
@@ -3504,6 +3504,22 @@ def _test_forward_expm1(shape):
     _test_forward_expm1([2, 5, 2, 5])
 
 
+def test_forward_softsign():
+    """test operator softsign """
+
+    def _test_forward_softsign(shape):
+        tf.disable_eager_execution()
+        np_data = np.random.uniform(1, 100, size=shape).astype(np.float32)
+        tf.reset_default_graph()
+        in_data = tf.placeholder(tf.float32, shape, name="in_data")
+        tf.nn.softsign(in_data, name="softsign")
+        compare_tf_with_tvm([np_data], ["in_data:0"], "softsign:0")
+
+    _test_forward_softsign([1, 100])
+    _test_forward_softsign([1, 10, 10])
+    _test_forward_softsign([2, 5, 2, 5])
+
+
 def test_forward_negative():
     """test tf operator Neg """
     np_data = np.random.uniform(-100, 255, size=(224, 224, 3)).astype(np.float32)