diff --git a/python/tvm/relay/frontend/oneflow.py b/python/tvm/relay/frontend/oneflow.py
index 4f278d8249e8..910567c66225 100644
--- a/python/tvm/relay/frontend/oneflow.py
+++ b/python/tvm/relay/frontend/oneflow.py
@@ -1025,15 +1025,17 @@ def _impl_v1(cls, inputs, attrs, params):
         return out
 
 
-class ThresholdedRelu(OneFlowOpConverter):
-    """Operator converter for ThresholdedRelu."""
+class Threshold(OneFlowOpConverter):
+    """Operator converter for Threshold."""
 
     @classmethod
     def _impl_v1(cls, inputs, attrs, params):
-        alpha = float(attrs.get("alpha", 1.0))
-        alpha_tensor = _op.full_like(inputs[0], fill_value=_expr.const(alpha))
-        mask = _op.greater(inputs[0], alpha_tensor).astype("float32")
-        return inputs[0] * mask
+        threshold = float(attrs.get("threshold_val", 1.0))
+        threshold_tensor = _op.full_like(inputs[0], fill_value=_expr.const(threshold))
+        value = float(attrs.get("value"))
+        value_tensor = _op.full_like(inputs[0], fill_value=_expr.const(value))
+        mask = _op.greater(inputs[0], threshold_tensor)
+        return _op.where(mask, inputs[0], value_tensor)
 
 
 class Elu(OneFlowOpConverter):
@@ -1422,6 +1424,7 @@ def get_convert_map():
         "relu": Renamer("relu"),
         "leaky_relu": Renamer("leaky_relu"),
         "prelu": PReLU.get_converter(),
+        "threshold": Threshold.get_converter(),
         "selu": Selu.get_converter(),
         "silu": Silu.get_converter(),
         "gelu": Gelu.get_converter(),
diff --git a/tests/python/frontend/oneflow/test_forward.py b/tests/python/frontend/oneflow/test_forward.py
index cc9333cd03bd..f7b9b934e124 100644
--- a/tests/python/frontend/oneflow/test_forward.py
+++ b/tests/python/frontend/oneflow/test_forward.py
@@ -24,6 +24,7 @@
 import tvm.testing
 import tvm.topi.testing
 from tvm import relay
+from packaging import version as package_version
 
 MODEL_HOME = "test_model"
 
@@ -702,6 +703,15 @@ def forward(self, x):
             x = x.softmax(dim=-1)
             return x
 
+    class Threshold(flow.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.active = flow.nn.Threshold(0.5, 0.2)
+
+        def forward(self, x):
+            x = self.active(x)
+            return x
+
     if os.path.exists(MODEL_HOME):
         rmdir(MODEL_HOME)
 
@@ -738,6 +748,11 @@ def forward(self, x):
             inputs=flow.tensor(np.random.rand(1, 12, 197, 197).astype(np.float32)),
         )
 
+    # Threshold was introduced in the version 0.8.0 of oneflow
+    if package_version.parse(flow.__version__) >= package_version.parse("0.8.0"):
+        model14 = Threshold().eval()
+        verify_activation(model14, device="llvm")
+
 
 @tvm.testing.uses_gpu
 def test_math():