diff --git a/paddle/phi/kernels/reduce_kernel_impl.cc b/paddle/phi/kernels/reduce_kernel_impl.cc
index 000cb99034c26a..93192480999039 100644
--- a/paddle/phi/kernels/reduce_kernel_impl.cc
+++ b/paddle/phi/kernels/reduce_kernel_impl.cc
@@ -20,10 +20,16 @@ namespace phi {
 // oneDNN's reduction kernel is optimized only for reducing throughout the
 // most outer dims, so in case of another type of reduction, it would be
 // better to fallback to native implementation
-inline bool HasOptimizedOneDNNKernel(const KernelContext* ctx) {
+inline bool HasOptimizedOneDNNKernel(const KernelContext* ctx,
+                                     const bool mean_op) {
   const DenseTensor& x = ctx->InputAt<phi::DenseTensor>(0);
-  const TensorRef& dims_tmp = ctx->AttrAt<TensorRef>(0);
-  IntArray dims_array = IntArray(*dims_tmp.Get());
+  IntArray dims_array;
+  if (mean_op) {
+    dims_array = ctx->AttrAt<IntArray>(0);
+  } else {
+    const TensorRef& dims_tmp = ctx->AttrAt<TensorRef>(0);
+    dims_array = IntArray(*dims_tmp.Get());
+  }
   int ndims = x.dims().size();
   const bool reduce_all = recompute_reduce_all(x, dims_array);
   auto dims = dims_array.GetData();
@@ -53,7 +59,15 @@ inline bool HasOptimizedOneDNNKernel(const KernelContext* ctx) {
 
 bool ReduceCheckIfOneDNNSupport(const KernelContext* ctx) {
   if (ctx->InputAt<phi::DenseTensor>(0).dims().size() > 5 ||
-      !HasOptimizedOneDNNKernel(ctx)) {
+      !HasOptimizedOneDNNKernel(ctx, false)) {
+    return false;
+  }
+  return true;
+}
+
+bool ReduceMeanCheckIfOneDNNSupport(const KernelContext* ctx) {
+  if (ctx->InputAt<phi::DenseTensor>(0).dims().size() > 5 ||
+      !HasOptimizedOneDNNKernel(ctx, true)) {
     return false;
   }
   return true;
diff --git a/paddle/phi/kernels/reduce_kernel_impl.h b/paddle/phi/kernels/reduce_kernel_impl.h
index aef4f57ddbdcff..e117f6ab335dd6 100644
--- a/paddle/phi/kernels/reduce_kernel_impl.h
+++ b/paddle/phi/kernels/reduce_kernel_impl.h
@@ -21,4 +21,6 @@ bool ReduceCheckIfOneDNNSupport(const KernelContext* ctx);
 
 bool ReduceGradCheckIfOneDNNSupport(const KernelContext* ctx);
 
+bool ReduceMeanCheckIfOneDNNSupport(const KernelContext* ctx);
+
 }  // namespace phi
diff --git a/paddle/phi/kernels/reduce_mean_kernel.cc b/paddle/phi/kernels/reduce_mean_kernel.cc
index 16b3abf0e29319..a657e7ba8c01d0 100644
--- a/paddle/phi/kernels/reduce_mean_kernel.cc
+++ b/paddle/phi/kernels/reduce_mean_kernel.cc
@@ -67,7 +67,7 @@ PD_REGISTER_KERNEL(mean, KPS, ALL_LAYOUT, phi::MeanKernel, float) {}
 #if defined(PADDLE_WITH_DNNL)
 PD_REGISTER_KERNEL(
     mean, OneDNN, ONEDNN, phi::MeanKernel, float, phi::dtype::bfloat16) {
-  kernel->check_if_onednn_kernel_support_ = phi::ReduceCheckIfOneDNNSupport;
+  kernel->check_if_onednn_kernel_support_ = phi::ReduceMeanCheckIfOneDNNSupport;
 }
 #endif
 
diff --git a/test/ir/pir/fused_pass/onednn/test_placement_pass_mean_op.py b/test/ir/pir/fused_pass/onednn/test_placement_pass_mean_op.py
new file mode 100644
index 00000000000000..6443a60c331f97
--- /dev/null
+++ b/test/ir/pir/fused_pass/onednn/test_placement_pass_mean_op.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import unittest
+
+import numpy as np
+from pass_test import PassTest
+
+import paddle
+
+paddle.enable_static()
+
+
+class TestMeanPlacementPass(PassTest):
+    def is_program_valid(self, program=None):
+        return True
+
+    def build_ir_program(self):
+        with paddle.pir_utils.IrGuard():
+            main_prog = paddle.static.Program()
+            start_prog = paddle.static.Program()
+            with paddle.pir.core.program_guard(main_prog, start_prog):
+                x = paddle.static.data(
+                    name='x', shape=[5, 2, 5, 5], dtype='float32'
+                )
+                mean = paddle.mean(x)
+                out = paddle.assign(mean)
+                self.pass_attr_list = [{'onednn_placement_pass': {}}]
+
+                self.feeds = {
+                    "x": np.random.random((5, 2, 5, 5)).astype("float32"),
+                }
+                self.fetch_list = [out]
+                self.valid_op_map = {
+                    "onednn_op.mean": 1,
+                }
+                return [main_prog, start_prog]
+
+    def sample_program(self):
+        yield self.build_ir_program(), False
+
+    def setUp(self):
+        self.places.append(paddle.CPUPlace())
+
+    def test_check_output(self):
+        self.check_pass_correct()
+
+
+if __name__ == "__main__":
+    unittest.main()