diff --git a/paconvert/api_mapping.json b/paconvert/api_mapping.json
index 00e31996a..71cdc5d20 100644
--- a/paconvert/api_mapping.json
+++ b/paconvert/api_mapping.json
@@ -3640,6 +3640,45 @@
       "inplace"
     ]
   },
+  "torch.nn.Embedding": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.Embedding",
+    "args_list": [
+      "num_embeddings",
+      "embedding_dim",
+      "padding_idx",
+      "max_norm",
+      "norm_type",
+      "scale_grad_by_freq",
+      "sparse"
+    ],
+    "unsupport_args": [
+      "max_norm",
+      "norm_type",
+      "scale_grad_by_freq"
+    ]
+  },
+  "torch.nn.functional.embedding": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.embedding",
+    "args_list": [
+      "input",
+      "weight",
+      "padding_idx",
+      "max_norm",
+      "norm_type",
+      "scale_grad_by_freq",
+      "sparse"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    },
+    "unsupport_args": [
+      "max_norm",
+      "norm_type",
+      "scale_grad_by_freq"
+    ]
+  },
   "torch.nn.Hardshrink": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nn.Hardshrink",
diff --git a/tests/test_nn_Embedding.py b/tests/test_nn_Embedding.py
new file mode 100644
index 000000000..0a7076035
--- /dev/null
+++ b/tests/test_nn_Embedding.py
@@ -0,0 +1,83 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.Embedding")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        embedding = torch.nn.Embedding(4, 3)
+        w0 = torch.Tensor([[0., 0., 0.],
+                    [1., 1., 1.],
+                    [2., 2., 2.],
+                    [3., 3., 3.]])
+        with torch.no_grad():
+            embedding.weight[0]=w0[0]
+            embedding.weight[1]=w0[1]
+            embedding.weight[3]=w0[3]
+        x = torch.LongTensor([[0],[1],[3]])
+        result = embedding(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        padding_idx = 0
+        embedding = torch.nn.Embedding(4, 3,padding_idx=padding_idx)
+        w0 = torch.Tensor([[0., 0., 0.],
+                    [1., 1., 1.],
+                    [2., 2., 2.],
+                    [3., 3., 3.]])
+        with torch.no_grad():
+            embedding.weight[0]=w0[0]
+            embedding.weight[1]=w0[1]
+            embedding.weight[2]=w0[2]
+            embedding.weight[3]=w0[3]
+        x = torch.LongTensor([[0],[1],[3]])
+        result = embedding(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        padding_idx = 0
+        embedding = torch.nn.Embedding(4, 3,padding_idx=padding_idx,max_norm=2.0)
+        w0 = torch.Tensor([[0., 0., 0.],
+                    [1., 1., 1.],
+                    [2., 2., 2.],
+                    [3., 3., 3.]])
+        with torch.no_grad():
+            embedding.weight[0]=w0[0]
+            embedding.weight[1]=w0[1]
+            embedding.weight[2]=w0[2]
+            embedding.weight[3]=w0[3]
+        x = torch.LongTensor([[0],[1],[3]])
+        result = embedding(x)
+        """
+    )
+    obj.run(pytorch_code, unsupport=True, reason="paddle unsupport")
diff --git a/tests/test_nn_functional_embedding.py b/tests/test_nn_functional_embedding.py
new file mode 100644
index 000000000..abc4a11e8
--- /dev/null
+++ b/tests/test_nn_functional_embedding.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.functional.embedding")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import numpy as np
+        embedding_matrix = torch.Tensor([[0., 0., 0.],
+                    [1., 1., 1.],
+                    [2., 2., 2.],
+                    [3., 3., 3.]])
+
+        x = torch.tensor(np.array([[0,1],[2,3]]))
+        result = torch.nn.functional.embedding(x,embedding_matrix)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import numpy as np
+        embedding_matrix = torch.Tensor([[0., 0., 0.],
+                    [1., 1., 1.],
+                    [2., 2., 2.],
+                    [3., 3., 3.]])
+
+        x = torch.tensor(np.array([[0,1],[2,3]]))
+        result = torch.nn.functional.embedding(x,embedding_matrix,padding_idx=0)
+        """
+    )
+
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        w0 = torch.Tensor([[0., 0., 0.],
+                    [1., 1., 1.],
+                    [2., 2., 2.],
+                    [3., 3., 3.]])
+        result = torch.nn.functional.embedding(x,embedding_matrix,padding_idx=0,max_norm=2)
+        """
+    )
+    obj.run(pytorch_code, unsupport=True, reason="paddle unsupport")