diff --git a/demo/text-classification/README.md b/demo/text-classification/README.md
index 6452b81e1ce39f4cfeb758484b0e3478a18e00f2..461ba6045be4037d871848a7dd1388accc4e04ab 100644
--- a/demo/text-classification/README.md
+++ b/demo/text-classification/README.md
@@ -111,7 +111,7 @@ strategy = hub.AdamWeightDecayStrategy(
     learning_rate=5e-5,
     weight_decay=0.01,
     warmup_proportion=0.0,
-    warmup_strategy="linear_warmup_decay",
+    lr_scheduler="linear_warmup_decay",
 )
 
 config = hub.RunConfig(use_cuda=True, num_epoch=3, batch_size=32, strategy=strategy)
@@ -124,7 +124,7 @@ hub.finetune_and_eval(task=cls_task, data_reader=reader, feed_list=feed_list, co
 `learning_rate`: Finetune过程中的最大学习率;
 `weight_decay`: 模型的正则项参数，默认0.01，如果模型有过拟合倾向，可适当调高这一参数;
 `warmup_proportion`: 如果warmup_proportion>0, 例如0.1, 则学习率会在前10%的steps中线性增长至最高值learning_rate;
-`warmup_strategy`: 有两种策略可选(1) `linear_warmup_decay`策略学习率会在最高点后以线性方式衰减; `noam_decay`策略学习率会在最高点以多项式形式衰减；
+`lr_scheduler`: 有两种策略可选(1) `linear_warmup_decay`策略学习率会在最高点后以线性方式衰减; `noam_decay`策略学习率会在最高点以多项式形式衰减；
 
 #### 运行配置
 `RunConfig` 主要控制Finetune的训练，包含以下可控制的参数:
diff --git a/demo/text-classification/text_classifier.py b/demo/text-classification/text_classifier.py
index b0e1042f64500d7dddc8c6db7efd51c226b7e31b..f6a09b86dbe1ee1514a5875ed54fa86e20c2fe6c 100644
--- a/demo/text-classification/text_classifier.py
+++ b/demo/text-classification/text_classifier.py
@@ -79,7 +79,7 @@ if __name__ == '__main__':
         strategy = hub.AdamWeightDecayStrategy(
             weight_decay=args.weight_decay,
             learning_rate=args.learning_rate,
-            warmup_strategy="linear_warmup_decay",
+            lr_scheduler="linear_warmup_decay",
         )
 
         # Setup runing config for PaddleHub Finetune API
diff --git a/paddlehub/finetune/strategy.py b/paddlehub/finetune/strategy.py
index f65288100d0cc9b304cb7424fbf32e1cd4625203..4166a018cc560ad85885accd5e4d4e45e687b3e0 100644
--- a/paddlehub/finetune/strategy.py
+++ b/paddlehub/finetune/strategy.py
@@ -64,23 +64,23 @@ class DefaultStrategy(object):
 class AdamWeightDecayStrategy(DefaultStrategy):
     def __init__(self,
                  learning_rate=1e-4,
-                 warmup_strategy="linear_warmup_decay",
+                 lr_scheduler="linear_warmup_decay",
                  warmup_proportion=0.0,
                  weight_decay=0.01,
                  optimizer_name=None):
         super().__init__(
             learning_rate=learning_rate, optimizer_name=optimizer_name)
         # check strategy correctness
-        if warmup_strategy not in ["linear_warmup_decay", "noam_decay"]:
-            raise ValueError("warmup strategy {} is not setup "
-                             "correctly".format(warmup_strategy))
-        self._warmup_strategy = warmup_strategy
+        if lr_scheduler not in ["linear_warmup_decay", "noam_decay"]:
+            raise ValueError("lr_scheduler {} is not setup "
+                             "correctly".format(lr_scheduler))
+        self._lr_scheduler = lr_scheduler
         self._warmup_proportion = warmup_proportion
         self._weight_decay = weight_decay
 
     @property
-    def warmup_strategy(self):
-        return self._warmup_strategy
+    def lr_scheduler(self):
+        return self._lr_scheduler
 
     @property
     def warmup_proportion(self):
@@ -99,7 +99,7 @@ class AdamWeightDecayStrategy(DefaultStrategy):
 
         scheduled_lr = adam_weight_decay_optimization(
             loss, warmup_steps, max_train_steps, self.learning_rate,
-            main_program, self.weight_decay, self.warmup_strategy)
+            main_program, self.weight_decay, self.lr_scheduler)
 
         return scheduled_lr