diff --git a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
index 6fb18a03ecd1f224d6ac86f2036c3900f20365c2..bcde0a103cbd431b29ad42dc60865ac3a4072a1b 100755
--- a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
+++ b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
@@ -62,7 +62,6 @@ def _rename_or_remove_weight(weights,
     if origin_name not in weights:
         raise KeyError('{} not a key in {}'.format(origin_name, weights.keys()))
     if is_remove:
-        # TODO There may be problems when the same data is used as an argument to multiple OPs.
         # remove weight
         data = weights.pop(origin_name)
     else:
@@ -182,6 +181,8 @@ class OpSet9():
         self.weights = dict()
         self.nn_name2id = dict()
         self.done_weight_list = list()
+        # solve for same data is used as an argument to multiple OPs.
+        self.rename_mapper = dict()
 
     @print_mapping_info
     def directly_map(self, node, *args, **kwargs):
@@ -1680,13 +1681,39 @@ class OpSet9():
         epsilon = node.get_attr('epsilon', 1e-5)
         c = val_x.out_shapes[0][1]
 
-        _rename_or_remove_weight(self.weights, val_scale.name,
-                                 op_name + '.weight')
-        _rename_or_remove_weight(self.weights, val_b.name, op_name + '.bias')
-        _rename_or_remove_weight(self.weights, val_var.name,
-                                 op_name + '._variance')
-        _rename_or_remove_weight(self.weights, val_mean.name,
-                                 op_name + '._mean')
+        # solved the same data is used as an argument to multiple OPs.
+        if val_scale.name in self.rename_mapper:
+            new_name = self.rename_mapper[val_scale.name]
+            _rename_or_remove_weight(self.weights, new_name,
+                                     op_name + '.weight', False)
+        else:
+            _rename_or_remove_weight(self.weights, val_scale.name,
+                                     op_name + '.weight')
+            self.rename_mapper[val_scale.name] = op_name + '.weight'
+        if val_b.name in self.rename_mapper:
+            new_name = self.rename_mapper[val_b.name]
+            _rename_or_remove_weight(self.weights, new_name, op_name + '.bias',
+                                     False)
+        else:
+            _rename_or_remove_weight(self.weights, val_b.name,
+                                     op_name + '.bias')
+            self.rename_mapper[val_b.name] = op_name + '.bias'
+        if val_var.name in self.rename_mapper:
+            new_name = self.rename_mapper[val_var.name]
+            _rename_or_remove_weight(self.weights, new_name,
+                                     op_name + '._variance', False)
+        else:
+            _rename_or_remove_weight(self.weights, val_var.name,
+                                     op_name + '._variance')
+            self.rename_mapper[val_var.name] = op_name + '._variance'
+        if val_mean.name in self.rename_mapper:
+            new_name = self.rename_mapper[val_mean.name]
+            _rename_or_remove_weight(self.weights, new_name, op_name + '._mean',
+                                     False)
+        else:
+            _rename_or_remove_weight(self.weights, val_mean.name,
+                                     op_name + '._mean')
+            self.rename_mapper[val_mean.name] = op_name + '._mean'
 
         # Attribute: spatial is used in BatchNormalization-1,6,7
         spatial = bool(node.get_attr('spatial'))