diff --git a/0003-fix-atune-adm-analyse-failed-problem.patch b/0003-fix-atune-adm-analyse-failed-problem.patch
new file mode 100644
index 0000000000000000000000000000000000000000..c7b2ece76fec77102f881750ec19e2d21ba43838
--- /dev/null
+++ b/0003-fix-atune-adm-analyse-failed-problem.patch
@@ -0,0 +1,54 @@
+From 2cc1896225cec667893068a3be5d8e7d09043af5 Mon Sep 17 00:00:00 2001
+From: Pshysimon <caixiaomeng2@huawei.com>
+Date: Sat, 12 Apr 2025 16:44:11 +0800
+Subject: [PATCH] fix atune-adm analyse failed problem
+
+---
+ analysis/optimizer/app_characterization.py | 16 ++++++++--------
+ 1 file changed, 8 insertions(+), 8 deletions(-)
+
+diff --git a/analysis/optimizer/app_characterization.py b/analysis/optimizer/app_characterization.py
+index 80b6752..738320f 100644
+--- a/analysis/optimizer/app_characterization.py
++++ b/analysis/optimizer/app_characterization.py
+@@ -202,16 +202,9 @@ class AppCharacterization(WorkloadCharacterization):
+         :param feature_selection:  whether to perform feature extraction
+         :param consider_perf: whether to consider perf indicators
+         """
+-        if consider_perf == None:
+-            consider_perf = self.consider_perf
+-            
+-        data_features = self.get_consider_perf(consider_perf)
+-        
+         cpu_exist, mem_exist, net_quality_exist, net_io_exist, disk_io_exist = self.bottleneck.search_bottleneck(data)
+         bottleneck_binary = (int(cpu_exist) << 4) | (int(mem_exist) << 3) | (int(net_quality_exist) << 2) | (int(net_io_exist) << 1) | int(disk_io_exist)
+ 
+-        data = data[data_features]
+-
+         tencoder_path = os.path.join(self.model_path, "tencoder.pkl")
+         aencoder_path = os.path.join(self.model_path, "aencoder.pkl")
+         scaler_path = os.path.join(self.model_path, "scaler.pkl")
+@@ -226,6 +219,13 @@ class AppCharacterization(WorkloadCharacterization):
+         type_model_clf = joblib.load(type_model_path)
+         app_model_clf = joblib.load(app_model_path)
+ 
++        if consider_perf is None: 
++            consider_perf = (len(self.scaler.mean_) == len(data.columns))
++
++        data_features = self.get_consider_perf(consider_perf)
++
++        data = data[data_features]
++
+         data = self.scaler.transform(data)
+ 
+         if feature_selection:
+@@ -290,4 +290,4 @@ class AppCharacterization(WorkloadCharacterization):
+         confidence = prediction[1] / len(result)
+         if confidence > 0.5:
+             return bottleneck_binary, prediction[0], confidence
+-        return bottleneck_binary, "default", confidence
+\ No newline at end of file
++        return bottleneck_binary, "default", confidence
+-- 
+2.27.0
+
diff --git a/atune.spec b/atune.spec
index a2b5f8c7dbad5c6c55c2d81e5a0e99f17375f2a6..1f420db30154ac121242142ed717cb0bdf8afa0e 100755
--- a/atune.spec
+++ b/atune.spec
@@ -3,7 +3,7 @@
 Summary: AI auto tuning system
 Name: atune
 Version: 1.2.0
-Release: 4
+Release: 5
 License: MulanPSL-2.0
 URL: https://gitee.com/openeuler/A-Tune
 Source: https://gitee.com/openeuler/A-Tune/repository/archive/v%{version}.tar.gz
@@ -16,6 +16,7 @@ Patch9004: import-os-for-app.py.patch
 Patch9005: adapt-sqlite-3.42-to-resolve-the-build-failure.patch
 Patch9006: 0001-fix-skopt.Optimizer-incompatible-with-numpy-1.24.patch
 Patch9007: 0002-scikit-learn-1.2-rename-the-parameter-base_estimator.patch
+Patch9008: 0003-fix-atune-adm-analyse-failed-problem.patch
 
 BuildRequires: rpm-build golang-bin procps-ng
 BuildRequires: sqlite >= 3.24.0 openssl
@@ -94,6 +95,7 @@ atune restful api for manage atuned AI tuning system.
 %patch9005 -p1
 %patch9006 -p1
 %patch9007 -p1
+%patch9008 -p1
 
 %build
 %make_build
@@ -194,6 +196,9 @@ atune restful api for manage atuned AI tuning system.
 %exclude /etc/atuned/rest_certs
 
 %changelog
+* Mon Apr 21 2025 caixiaomeng <caixiaomeng2@huawei.com> - 1.2.0-5
+- fix atune-adm analyse failed problem
+
 * Thu Mar 20 2025 dongjiao <dongjiao@kylinos.cn> - 1.2.0-4
 - fix bug cause scikit-learn-1.2 rename the parameter 'base_estimator' to 'estimator'.