diff --git a/paddlehub/finetune/task/classifier_task.py b/paddlehub/finetune/task/classifier_task.py
index b137afdc750737a6927a981014817d6b6383c36a..f3f700ed9853fa7a92aca8c30f482b64bbfd8686 100644
--- a/paddlehub/finetune/task/classifier_task.py
+++ b/paddlehub/finetune/task/classifier_task.py
@@ -335,7 +335,6 @@ class MultiLabelClassifierTask(ClassifierTask):
             config=config,
             hidden_units=hidden_units,
             metrics_choices=metrics_choices)
-        self.class_name = list(data_reader.label_map.keys())
 
     def _build_net(self):
         cls_feats = fluid.layers.dropout(
@@ -415,7 +414,8 @@ class MultiLabelClassifierTask(ClassifierTask):
                 # NOTE: for MultiLabelClassifierTask, the metrics will be used to evaluate all the label
                 #      and their mean value will also be reported.
                 for index, auc in enumerate(auc_list):
-                    scores["auc_" + self.class_name[index]] = auc_list[index][0]
+                    scores["auc_" + self._base_data_reader.dataset.
+                           label_list[index]] = auc_list[index][0]
             else:
                 raise ValueError("Not Support Metric: \"%s\"" % metric)
         return scores, avg_loss, run_speed
@@ -428,7 +428,6 @@ class MultiLabelClassifierTask(ClassifierTask):
 
     def _postprocessing(self, run_states):
         results = []
-        label_list = list(self._base_data_reader.label_map.keys())
         for batch_state in run_states:
             batch_result = batch_state.run_results
             for sample_id in range(len(batch_result[0])):
@@ -437,7 +436,9 @@ class MultiLabelClassifierTask(ClassifierTask):
                         self._base_data_reader.dataset.num_labels):
                     sample_category_prob = batch_result[category_id][sample_id]
                     sample_category_value = np.argmax(sample_category_prob)
-                    sample_result.append(
-                        {label_list[category_id]: sample_category_value})
+                    sample_result.append({
+                        self._base_data_reader.dataset.label_list[category_id]:
+                        sample_category_value
+                    })
                 results.append(sample_result)
         return results