diff --git a/doc/fluid/api_cn/optimizer_cn.rst b/doc/fluid/api_cn/optimizer_cn.rst
index 8cd66fe68521473c988947a89b7ba50f34042dcf..3fb30f96e02eaadda8a566e7cc8f34cdfbeb13ac 100644
--- a/doc/fluid/api_cn/optimizer_cn.rst
+++ b/doc/fluid/api_cn/optimizer_cn.rst
@@ -32,3 +32,4 @@ fluid.optimizer
     optimizer_cn/RMSPropOptimizer_cn.rst
     optimizer_cn/SGD_cn.rst
     optimizer_cn/SGDOptimizer_cn.rst
+    optimizer_cn/LookaheadOptimizer_cn.rst
diff --git a/doc/fluid/api_cn/optimizer_cn/LookaheadOptimizer_cn.rst b/doc/fluid/api_cn/optimizer_cn/LookaheadOptimizer_cn.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4088deb29bc284e455437890236f6d102f6fe410
--- /dev/null
+++ b/doc/fluid/api_cn/optimizer_cn/LookaheadOptimizer_cn.rst
@@ -0,0 +1,51 @@
+.. _cn_api_fluid_optimizer_LookaheadOptimizer:
+
+LookaheadOptimizer
+-------------------------------
+
+.. py:class:: paddle.fluid.optimizer.LookaheadOptimizer(inner_optimizer, alpha=0.5, k=5)
+
+本类实现了Lookahead优化算法：https://arxiv.org/abs/1907.08610。Lookahead优化算法在内存中保存两部分参数：快参数和慢参数。每个训练步次，inner_optimizer都更新快参数；每隔k个训练步次，Lookahead更新慢参数，如下：
+
+.. math::
+
+  & slow\_param_t = slow\_param_{t-1} + \alpha * (fast\_param_{t-1} - slow\_param_{t-1})
+
+  & fast\_param_t = slow\_param_t
+
+参数:
+    - **inner_optimizer** (Optimizer) - 基础优化器，如SGD
+    - **alpha** (float) - Lookahead 的学习率
+    - **k** (int) - 慢参数更新的频率：k次一更新
+
+**代码示例**
+
+.. code-block:: python
+
+            import paddle
+            import paddle.fluid as fluid
+            import numpy as np
+
+            x = fluid.layers.data(name='x', shape=[2], dtype='float32')
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+            y = fluid.layers.fc(input=[x], size=2, act="softmax")
+            loss = fluid.layers.cross_entropy(input=y, label=label)
+            loss = fluid.layers.mean(x=loss)
+            sgd = fluid.optimizer.SGD(learning_rate=0.01)
+            optimizer = fluid.optimizer.LookaheadOptimizer(sgd,
+                                            alpha=0.5,
+                                            k=5)
+            optimizer.minimize(loss)
+            main_program = fluid.default_main_program()
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(fluid.default_startup_program())
+
+            feeder = fluid.DataFeeder(feed_list=[x, label], place=place)
+
+            step = 0
+            while(step < 10):
+                step += 1
+                exe.run(fluid.default_main_program(),
+                feed=feeder.feed(batch_data))
+