diff --git a/deploy/cpp/src/paddlex.cpp b/deploy/cpp/src/paddlex.cpp
index fb7c12c2e47b6bdc030ccef36bac1277e021436e..b3e292c23e781d675ad7e23512fe96672d4b8121 100644
--- a/deploy/cpp/src/paddlex.cpp
+++ b/deploy/cpp/src/paddlex.cpp
@@ -65,6 +65,15 @@ bool Model::load_config(const std::string& model_dir) {
   YAML::Node config = YAML::LoadFile(yaml_file);
   type = config["_Attributes"]["model_type"].as<std::string>();
   name = config["Model"].as<std::string>();
+  std::string version = config["version"].as<std::string>();
+  if (version[0] == '0') {
+    std::cerr << "[Init] Version of the loaded model is lower than 1.0.0, deployment "
+              << "cannot be done, please refer to "
+              << "https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/tutorials/deploy/upgrade_version.md "
+              << "to transfer version."
+              << std::endl;
+    return false;
+  }
   bool to_rgb = true;
   if (config["TransformsMode"].IsDefined()) {
     std::string mode = config["TransformsMode"].as<std::string>();
diff --git a/docs/tutorials/deploy/deploy_lite.md b/docs/tutorials/deploy/deploy_lite.md
index 392e945dea2465ca4f6f40f2a131f7cad19db03a..0c4b1ec3600a90f8d112f0bf2487a2ee063b74fb 100644
--- a/docs/tutorials/deploy/deploy_lite.md
+++ b/docs/tutorials/deploy/deploy_lite.md
@@ -9,6 +9,7 @@ pip install paddlelite
 step 2: 将PaddleX模型导出为inference模型
 
 参考[导出inference模型](deploy_server/deploy_python.html#inference)将模型导出为inference格式模型。
+**注意：由于PaddleX代码的持续更新，版本低于1.0.0的模型暂时无法直接用于预测部署，参考[模型版本升级](../upgrade_version.md)对模型版本进行升级。**
 
 step 3: 将inference模型转换成PaddleLite模型
 
diff --git a/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_linux.md b/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_linux.md
index b4edf3510ae992d72ea60e1078f22e12d54357c2..b86ca4dc864edb6bdf0d4a73f9d90596cc9ea2b8 100755
--- a/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_linux.md
+++ b/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_linux.md
@@ -104,7 +104,8 @@ make
 
 ### Step5: 预测及可视化
 
-参考[导出inference模型](../deploy_python.html#inference)将模型导出为inference格式模型。
+参考[导出inference模型](../../deploy_python.html#inference)将模型导出为inference格式模型。
+**注意：由于PaddleX代码的持续更新，版本低于1.0.0的模型暂时无法直接用于预测部署，参考[模型版本升级](../upgrade_version.md)对模型版本进行升级。**
 
 编译成功后，预测demo的可执行程序分别为`build/demo/detector`，`build/demo/classifer`，`build/demo/segmenter`，用户可根据自己的模型类型选择，其主要命令参数说明如下：
 
diff --git a/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_win_vs2019.md b/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_win_vs2019.md
index 2f7c62766291410ec8e48a77b7e814edeb1523bb..0d0bea96244c86913ebb8116a63b5d0a6143f1a4 100755
--- a/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_win_vs2019.md
+++ b/docs/tutorials/deploy/deploy_server/deploy_cpp/deploy_cpp_win_vs2019.md
@@ -100,6 +100,7 @@ PaddlePaddle C++ 预测库针对不同的`CPU`，`CUDA`，以及是否支持Tens
 ### Step5: 预测及可视化
 
 参考[导出inference模型](../deploy_python.html#inference)将模型导出为inference格式模型。
+**注意：由于PaddleX代码的持续更新，版本低于1.0.0的模型暂时无法直接用于预测部署，参考[模型版本升级](../upgrade_version.md)对模型版本进行升级。**
 
 上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release`目录下，打开`cmd`，并切换到该目录：
 
diff --git a/docs/tutorials/deploy/deploy_server/deploy_python.md b/docs/tutorials/deploy/deploy_server/deploy_python.md
index c597f87cdbbc208ad2b72a8305642da41b9be5cd..321d48077fd0478234e8ce6386c7355c36d1c63c 100644
--- a/docs/tutorials/deploy/deploy_server/deploy_python.md
+++ b/docs/tutorials/deploy/deploy_server/deploy_python.md
@@ -20,6 +20,8 @@ paddlex --export_inference --model_dir=./xiaoduxiong_epoch_12 --save_dir=./infer
 ```
 
 ## 预测部署
+**注意：由于PaddleX代码的持续更新，版本低于1.0.0的模型暂时无法直接用于预测部署，参考[模型版本升级](../upgrade_version.md)对模型版本进行升级。**
+
 > 点击下载测试图片 [xiaoduxiong_test_image.tar.gz](https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_test_image.tar.gz)
 
 ```
diff --git a/docs/tutorials/deploy/deploy_server/encryption.md b/docs/tutorials/deploy/deploy_server/encryption.md
index 71b07048ed8372b4c4b9aa0b2082dc9ed9f2f9a0..7090421823bb3bbe2017818a3fc2f7e96608dda9 100644
--- a/docs/tutorials/deploy/deploy_server/encryption.md
+++ b/docs/tutorials/deploy/deploy_server/encryption.md
@@ -61,7 +61,7 @@ paddlex-encryption
 ./paddlex-encryption/tool/paddlex_encrypt_tool -model_dir /path/to/paddlex_inference_model -save_dir /path/to/paddlex_encrypted_model
 ```
 
-`-model_dir`用于指定inference模型路径（参考[导出inference模型](deploy_python.html#inference)将模型导出为inference格式模型），可使用[导出小度熊识别模型](deploy_python.html#inference)中导出的`inference_model`。加密完成后，加密过的模型会保存至指定的`-save_dir`下，包含`__model__.encrypted`、`__params__.encrypted`和`model.yml`三个文件，同时生成密钥信息，命令输出如下图所示，密钥为`kLAl1qOs5uRbFt0/RrIDTZW2+tOf5bzvUIaHGF8lJ1c=`
+`-model_dir`用于指定inference模型路径（参考[导出inference模型](deploy_python.html#inference)将模型导出为inference格式模型），可使用[导出小度熊识别模型](deploy_python.html#inference)中导出的`inference_model`（**注意**：由于PaddleX代码的持续更新，版本低于1.0.0的模型暂时无法直接用于预测部署，参考[模型版本升级](../upgrade_version.md)对模型版本进行升级。)。加密完成后，加密过的模型会保存至指定的`-save_dir`下，包含`__model__.encrypted`、`__params__.encrypted`和`model.yml`三个文件，同时生成密钥信息，命令输出如下图所示，密钥为`kLAl1qOs5uRbFt0/RrIDTZW2+tOf5bzvUIaHGF8lJ1c=`
 
 ![](../images/encrypt.png)