diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index caa6a9a93141f..8ca3427168577 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -17,7 +17,6 @@
     is_scalar,
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
-from pandas.core.dtypes.generic import ABCCategorical, ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import accessor
@@ -193,7 +192,9 @@ def __new__(
                     raise cls._scalar_data_error(data)
                 data = []
 
-        data = cls._create_categorical(data, dtype=dtype)
+        assert isinstance(dtype, CategoricalDtype), dtype
+        if not isinstance(data, Categorical) or data.dtype != dtype:
+            data = Categorical(data, dtype=dtype)
 
         data = data.copy() if copy else data
 
@@ -223,37 +224,11 @@ def _create_from_codes(self, codes, dtype=None, name=None):
         return CategoricalIndex(cat, name=name)
 
     @classmethod
-    def _create_categorical(cls, data, dtype=None):
-        """
-        *this is an internal non-public method*
-
-        create the correct categorical from data and the properties
-
-        Parameters
-        ----------
-        data : data for new Categorical
-        dtype : CategoricalDtype, defaults to existing
-
-        Returns
-        -------
-        Categorical
-        """
-        if isinstance(data, (cls, ABCSeries)) and is_categorical_dtype(data):
-            data = data.values
-
-        if not isinstance(data, ABCCategorical):
-            return Categorical(data, dtype=dtype)
-
-        if isinstance(dtype, CategoricalDtype) and dtype != data.dtype:
-            # we want to silently ignore dtype='category'
-            data = data._set_dtype(dtype)
-        return data
-
-    @classmethod
-    def _simple_new(cls, values, name=None, dtype=None):
+    def _simple_new(cls, values: Categorical, name=None, dtype=None):
+        # GH#32204 dtype is included for compat with Index._simple_new
+        assert isinstance(values, Categorical), type(values)
         result = object.__new__(cls)
 
-        values = cls._create_categorical(values, dtype=dtype)
         result._data = values
         result.name = name
 
@@ -295,7 +270,8 @@ def _is_dtype_compat(self, other) -> bool:
             values = other
             if not is_list_like(values):
                 values = [values]
-            other = CategoricalIndex(self._create_categorical(other, dtype=self.dtype))
+            cat = Categorical(other, dtype=self.dtype)
+            other = CategoricalIndex(cat)
             if not other.isin(values).all():
                 raise TypeError(
                     "cannot append a non-category item to a CategoricalIndex"
diff --git a/pandas/tests/indexes/categorical/test_constructors.py b/pandas/tests/indexes/categorical/test_constructors.py
index 1df0874e2f947..ee3f85da22781 100644
--- a/pandas/tests/indexes/categorical/test_constructors.py
+++ b/pandas/tests/indexes/categorical/test_constructors.py
@@ -136,12 +136,3 @@ def test_construction_with_categorical_dtype(self):
 
         with pytest.raises(ValueError, match=msg):
             Index(data, ordered=ordered, dtype=dtype)
-
-    def test_create_categorical(self):
-        # GH#17513 The public CI constructor doesn't hit this code path with
-        # instances of CategoricalIndex, but we still want to test the code
-        ci = CategoricalIndex(["a", "b", "c"])
-        # First ci is self, second ci is data.
-        result = CategoricalIndex._create_categorical(ci, ci)
-        expected = Categorical(["a", "b", "c"])
-        tm.assert_categorical_equal(result, expected)