diff --git a/x2paddle/op_mapper/pytorch2paddle/aten.py b/x2paddle/op_mapper/pytorch2paddle/aten.py
index dce8581d7699c3dcdab78fa1b87fbe0e8bc19157..1b7c5e79650547d28fc2d64d531b87a0ca947f14 100755
--- a/x2paddle/op_mapper/pytorch2paddle/aten.py
+++ b/x2paddle/op_mapper/pytorch2paddle/aten.py
@@ -2806,13 +2806,13 @@ def aten_hardswish(mapper, graph, node):
 
 
 def aten_index(mapper, graph, node):
-    """ 构造选择元素的PaddleLayer。
-    TorchScript示例:
+    """
+    TorchScript Code:
         %1681 : Float = aten::index(%1653, %1680)
-        参数含义:
-        %1681 (Tensor): 输出，选择后的Tensor。
-        %1653 (Tensor): 需要选择的Tensor。
-        %1680 (int): 选择的索引。
+        Parameter meaning:
+        %1681 (Tensor): Output Tensor
+        %1653 (Tensor): Input Tensor
+        %1680 (int): Index
     """
     scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
@@ -2820,31 +2820,25 @@ def aten_index(mapper, graph, node):
     layer_inputs = {}
     layer_attrs = {}
     inputs_name, inputs_node = mapper._get_inputs_name(node)
-    # 获取当前节点输出的list
+    # output list
     current_outputs = [output_name]
-    # 处理输入0，即%1653
+    # process Input Tensor
     mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
                         scope_name)
     layer_inputs["x"] = inputs_name[0]
-    # 处理输入1，即%1680
+    # process Index
     mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
                         scope_name)
     layer_inputs["index"] = inputs_name[1]
-    # 获取当前节点输入的list
+
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
         "prim.getitem",
-        inputs={"list": layer_inputs["index"]},
-        outputs=[layer_inputs["index"]],
-        scope_name=scope_name,
-        index=0)
-    graph.add_layer(
-        "paddle.index_select",
-        inputs=layer_inputs,
+        inputs={"list": layer_inputs["x"]},
         outputs=layer_outputs,
         scope_name=scope_name,
-        **layer_attrs)
+        index=layer_inputs["index"])
     return current_inputs, current_outputs