diff --git a/.vscode/launch.json b/.vscode/launch.json
index 14b7790..053641d 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -5,7 +5,7 @@
   "version": "0.2.0",
   "configurations": [
     {
-      "name": "Python Debugger: Current File",
+      "name": "Python Debugger: Current this project",
       "type": "debugpy",
       "request": "launch",
       "program": "main.py",
diff --git a/README.md b/README.md
index a8afbe3..2a2799c 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,50 @@
 # Deeplearning
+## Quickly Start
+1. 将项目文件夹编辑成**日期+项目名**
+2. 编辑好label名称，label名称命名变成英文或者数字
+>例如：”PDMS“， ”1“ 等, , 如果你的每一个类，下面又多个子特征则可以建立一个文件夹，在创建神经网络类的时候将**isDir**参数改成True即可.
+>详细如下图：
+>2.1. 如果只有一类特征
+>![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/image.png)
+>2.2. 如果有多类特征
+>![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/image.png)
 
-1. 使用前先建立一个分支，分支名字以日期加项目英文名称，比如`20241110Deeplearning`，方便回溯。
+3. 将准备好的文件夹移动到**Static**文件夹中（没有就建立），如果没有 **Result** 建立一个**Result**文件夹用来存放结果
+
+4. 读取数据：
+```python
+  # 以MaterialDiv为例
+  projet_name = '20241009MaterialDiv'                                           
+  label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
+  # 使用库 divSet 划分训练集和数据集
+  data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
+  X_train, X_test, y_train, y_test, encoder = divSet(
+    data=data, labels=label_names, test_size= 0.3
+  )
+```
+5. 创建神经网络类
+```python
+   model = Qmlp(
+     X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
+     hidden_layers = [128],
+     dropout_rate=0
+   )
+```
+
+6. 训练并获取数据
+```python
+  pca_2d, pca_3d = model.get_PCA()
+
+  model.fit(300)
+  
+  cm = model.get_cm()
+  epoch_data = model.get_epoch_data()
+
+  save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
+  save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
+  save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
+  save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
+
+```