diff --git a/docs/component/data.rst b/docs/component/data.rst
index eeddaec60d0..b57c032a567 100644
--- a/docs/component/data.rst
+++ b/docs/component/data.rst
@@ -433,7 +433,7 @@ The ``Processor`` module in ``Qlib`` is designed to be learnable and it is respo
 
 - ``DropnaProcessor``: `processor` that drops N/A features.
 - ``DropnaLabel``: `processor` that drops N/A labels.
-- ``TanhProcess``: `processor` that uses `tanh` to process noise data.
+- ``TanhProcess``: `processor` that applies ``tanh(x - 1)`` to denoise values. By default it transforms the ``feature`` column group only (via ``fields_group``), leaving labels unchanged.
 - ``ProcessInf``: `processor` that handles infinity values, it will be replaces by the mean of the column.
 - ``Fillna``: `processor` that handles N/A values, which will fill the N/A value by 0 or other given number.
 - ``MinMaxNorm``: `processor` that applies min-max normalization.
diff --git a/qlib/data/dataset/processor.py b/qlib/data/dataset/processor.py
index d05dbe381c5..c900e73bdcb 100644
--- a/qlib/data/dataset/processor.py
+++ b/qlib/data/dataset/processor.py
@@ -144,18 +144,31 @@ def readonly(self):
 
 
 class TanhProcess(Processor):
-    """Use tanh to process noise data"""
+    """Apply ``tanh(x - 1)`` to denoise feature values.
 
-    def __call__(self, df):
-        def tanh_denoise(data):
-            mask = data.columns.get_level_values(1).str.contains("LABEL")
-            col = df.columns[~mask]
-            data[col] = data[col] - 1
-            data[col] = np.tanh(data[col])
+    Qlib handlers (e.g. Alpha158/Alpha360) use MultiIndex columns whose first
+    level is the group name (``feature`` / ``label``) and whose second level is
+    the column name (e.g. ``LABEL0``). Selecting columns by matching ``"LABEL"``
+    on level 1 is brittle and inconsistent with other processors.
 
-            return data
+    This processor follows the same ``fields_group`` pattern as
+    :class:`Fillna`, :class:`CSZScoreNorm`, etc., so labels are left untouched
+    by default.
+
+    Parameters
+    ----------
+    fields_group : str, optional
+        Column group to transform. If ``None``, all columns are transformed.
+        Default is ``"feature"``.
+    """
 
-        return tanh_denoise(df)
+    def __init__(self, fields_group: Optional[str] = "feature"):
+        self.fields_group = fields_group
+
+    def __call__(self, df: pd.DataFrame):
+        cols = get_group_columns(df, self.fields_group)
+        df[cols] = np.tanh(df[cols] - 1)
+        return df
 
 
 class ProcessInf(Processor):
diff --git a/tests/data_mid_layer_tests/test_processor.py b/tests/data_mid_layer_tests/test_processor.py
index 46453b31628..94ad6ccd93c 100644
--- a/tests/data_mid_layer_tests/test_processor.py
+++ b/tests/data_mid_layer_tests/test_processor.py
@@ -3,9 +3,70 @@
 
 import unittest
 import numpy as np
+import pandas as pd
 from qlib.data import D
 from qlib.tests import TestAutoData
-from qlib.data.dataset.processor import MinMaxNorm, ZScoreNorm, CSZScoreNorm, CSZFillna
+from qlib.data.dataset.processor import MinMaxNorm, ZScoreNorm, CSZScoreNorm, CSZFillna, TanhProcess
+
+
+class TestTanhProcess(unittest.TestCase):
+    """Unit tests for TanhProcess (issue #1687).
+
+    These tests do not require downloaded market data.
+    """
+
+    @staticmethod
+    def _make_handler_like_df():
+        # Match Alpha158/Alpha360 MultiIndex layout:
+        # level 0 = group (feature/label), level 1 = column name.
+        columns = pd.MultiIndex.from_tuples(
+            [("feature", "RESI5"), ("feature", "WVMA5"), ("label", "LABEL0")],
+            names=["field", "name"],
+        )
+        index = pd.MultiIndex.from_tuples(
+            [("2023-01-01", "SH600000"), ("2023-01-02", "SH600000")],
+            names=["datetime", "instrument"],
+        )
+        data = np.array([[2.0, 3.0, 0.5], [4.0, 5.0, 0.8]], dtype=float)
+        return pd.DataFrame(data, index=index, columns=columns)
+
+    def test_default_transforms_feature_group_only(self):
+        df = self._make_handler_like_df()
+        label_before = df[("label", "LABEL0")].copy()
+
+        result = TanhProcess()(df)
+
+        pd.testing.assert_series_equal(result[("label", "LABEL0")], label_before)
+        np.testing.assert_allclose(
+            result[("feature", "RESI5")].to_numpy(),
+            np.tanh(np.array([2.0, 4.0]) - 1),
+            atol=1e-7,
+        )
+        np.testing.assert_allclose(
+            result[("feature", "WVMA5")].to_numpy(),
+            np.tanh(np.array([3.0, 5.0]) - 1),
+            atol=1e-7,
+        )
+
+    def test_fields_group_none_transforms_all_columns(self):
+        df = self._make_handler_like_df()
+        expected = np.tanh(df.to_numpy() - 1)
+
+        result = TanhProcess(fields_group=None)(df)
+
+        np.testing.assert_allclose(result.to_numpy(), expected, atol=1e-7)
+
+    def test_legacy_label_substring_mask_was_incorrect_for_standard_groups(self):
+        # Regression: matching "LABEL" on level 1 happens to catch LABEL0, but
+        # matching on level 0 (as suggested in some reports) would miss lowercase
+        # "label" and transform labels. fields_group avoids both pitfalls.
+        df = self._make_handler_like_df()
+        level0_mask = df.columns.get_level_values(0).str.contains("LABEL")
+        self.assertFalse(level0_mask.any())
+
+        label_before = df[("label", "LABEL0")].copy()
+        TanhProcess(fields_group="feature")(df)
+        pd.testing.assert_series_equal(df[("label", "LABEL0")], label_before)
 
 
 class TestProcessor(TestAutoData):