diff --git a/doc/doc_ch/angle_class.md b/doc/doc_ch/angle_class.md
index b2118661..d6a36b86 100644
--- a/doc/doc_ch/angle_class.md
+++ b/doc/doc_ch/angle_class.md
@@ -45,7 +45,7 @@ train_data/cls/word_002.jpg   180
 ```
 |-train_data
     |-cls
-        |- 和一个cls_gt_test.txt
+        |- cls_gt_test.txt
         |- test
             |- word_001.jpg
             |- word_002.jpg
@@ -62,29 +62,36 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本。
 *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
 
 ```
-# 设置PYTHONPATH路径
-export PYTHONPATH=$PYTHONPATH:.
-# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-# 启动训练
-python3 tools/train.py -c configs/cls/cls_mv3.yml
+# GPU训练 支持单卡，多卡训练，通过selected_gpus指定卡号
+# 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可
+python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/cls/cls_mv3.yml
 ```
 
 - 数据增强
 
-PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
+PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中取消`Train.dataset.transforms`下的`RecAug`和`RandAugment`字段的注释。
 
 默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。
 
 训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考：
-[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
-[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py) 
+[randaugment.py](../../ppocr/data/imaug/randaugment.py)
 
 *由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
 
 ### 训练
 
-PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/cls_mv3/best_accuracy` 。
+PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。训练过程中将会保存如下内容：
+```bash
+├── best_accuracy.pdopt # 最佳模型的优化器参数
+├── best_accuracy.pdparams # 最佳模型的参数
+├── best_accuracy.states # 最佳模型的指标和epoch等信息
+├── config.yml # 本次实验的配置文件
+├── latest.pdopt # 最新模型的优化器参数
+├── latest.pdparams # 最新模型的参数
+├── latest.states # 最新模型的指标和epoch等信息
+└── train.log # 训练日志
+```
 
 如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
 
@@ -92,9 +99,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml`
 
 ### 评估
 
-评估数据集可以通过`configs/cls/cls_reader.yml`  修改EvalReader中的 `label_file_path` 设置。
+评估数据集可以通过修改`configs/cls/cls_mv3.yml`文件里的`Eval.dataset.label_file_list` 字段设置。
 
-*注意* 评估时必须确保配置文件中 infer_img 字段为空
 ```
 export CUDA_VISIBLE_DEVICES=0
 # GPU 评估， Global.checkpoints 为待测权重
@@ -107,21 +113,20 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
 
 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
 
-默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重：
+通过 `Global.infer_img` 指定预测图片或文件夹路径，通过 `Global.checkpoints` 指定权重：
 
 ```
 # 预测分类结果
-python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
 ```
 
 预测图片：
 
-![](../imgs_words/en/word_1.png)
+![](../imgs_words/ch/word_1.jpg)
 
 得到输入图像的预测结果：
 
 ```
-infer_img: doc/imgs_words/en/word_1.png
-    scores: [[0.93161047 0.06838956]]
-    label: [0]
+infer_img: doc/imgs_words/ch/word_1.jpg
+     result: ('0', 0.9998784)
 ```
diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md
index c7fff3a1..defdff3c 100644
--- a/doc/doc_en/angle_class_en.md
+++ b/doc/doc_en/angle_class_en.md
@@ -65,26 +65,35 @@ Start training:
 ```
 # Set PYTHONPATH path
 export PYTHONPATH=$PYTHONPATH:.
-# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-# Training icdar15 English data
-python3 tools/train.py -c configs/cls/cls_mv3.yml
+# GPU training Support single card and multi-card training, specify the card number through selected_gpus
+# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
+python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/cls/cls_mv3.yml
 ```
 
 - Data Augmentation
 
-PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
+PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, Please uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` in the configuration file.
 
 The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
 
 Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
-[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
-[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py) 
+[randaugment.py](../../ppocr/data/imaug/randaugment.py)
 
 
 - Training
 
-PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/cls_mv3/best_accuracy` during the evaluation process.
+PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 1000 iter. The following content will be saved during training:
+```bash
+├── best_accuracy.pdopt # Optimizer parameters for the best model
+├── best_accuracy.pdparams # Parameters of the best model
+├── best_accuracy.states # Metric info and epochs of the best model
+├── config.yml # Configuration file for this experiment
+├── latest.pdopt # Optimizer parameters for the latest model
+├── latest.pdparams # Parameters of the latest model
+├── latest.states # Metric info and epochs of the latest model
+└── train.log # Training log
+```
 
 If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
 
@@ -92,7 +101,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
 
 ### EVALUATION
 
-The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader.
+The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
 
 ```
 export CUDA_VISIBLE_DEVICES=0
@@ -106,21 +115,20 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
 
 Using the model trained by paddleocr, you can quickly get prediction through the following script.
 
-The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
+Use `Global.infer_img` to specify the path of the predicted picture or folder, and use `Global.checkpoints` to specify the weight:
 
 ```
 # Predict English results
-python3 tools/infer_rec.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_10.png
 ```
 
 Input image:
 
-![](../imgs_words/en/word_1.png)
+![](../imgs_words_en/word_10.png)
 
 Get the prediction result of the input image:
 
 ```
-infer_img: doc/imgs_words/en/word_1.png
-    scores: [[0.93161047 0.06838956]]
-    label: [0]
+infer_img: doc/imgs_words_en/word_10.png
+     result: ('0', 0.9999995)
 ```