diff --git a/docs/en/index.rst b/docs/en/index.rst
index a3d2a8515..0bb56602b 100644
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -102,6 +102,7 @@ We always welcome *PRs* and *Issues* for the betterment of MMPretrain.
useful_tools/verify_dataset.md
useful_tools/log_result_analysis.md
useful_tools/complexity_analysis.md
+ useful_tools/confusion_matrix.md
.. toctree::
:maxdepth: 1
diff --git a/docs/en/useful_tools/confusion_matrix.md b/docs/en/useful_tools/confusion_matrix.md
new file mode 100644
index 000000000..306b585c0
--- /dev/null
+++ b/docs/en/useful_tools/confusion_matrix.md
@@ -0,0 +1,84 @@
+# Confusion Matrix
+
+MMPretrain provides `tools/analysis_tools/confusion_matrix.py` tool to calculate and visualize the confusion matrix. For an introduction to the confusion matrix, see [link](https://en.wikipedia.org/wiki/Confusion_matrix).
+
+## Command-line Usage
+
+**Command**：
+
+```shell
+python tools/analysis_tools/confusion_matrix.py \
+ ${CONFIG_FILE} \
+ ${CHECKPOINT} \
+ [--show] \
+ [--show-path] \
+ [--include-values] \
+ [--cmap ${CMAP}] \
+ [--cfg-options ${CFG-OPTIONS}]
+```
+
+**Description of all arguments**：
+
+- `config`: The path of the model config file.
+- `checkpoint`: The path of the checkpoint.
+- `--show`: If or not to show the matplotlib visualization result of the confusion matrix, the default is `False`.
+- `--show-path`: If `show` is True, the path where the results are saved is visualized.
+- `--include-values`: Whether to add values to the visualization results.
+- `--cmap`: The color map used for visualization results, `cmap`, which defaults to `viridis`.
+
+* `--cfg-options`: Modifications to the configuration file, refer to [Learn about Configs](../user_guides/config.md).
+
+**Examples of use**:
+
+```shell
+python tools/analysis_tools/confusion_matrix.py \
+ configs/resnet/resnet50_8xb16_cifar10.py \
+ https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth \
+ --show
+```
+
+**output image**:
+
+
+
+## **Basic Usage**
+
+```python
+>>> import torch
+>>> from mmpretrain.evaluation import ConfusionMatrix
+>>> y_pred = [0, 1, 1, 3]
+>>> y_true = [0, 2, 1, 3]
+>>> ConfusionMatrix.calculate(y_pred, y_true, num_classes=4)
+tensor([[1, 0, 0, 0],
+ [0, 1, 0, 0],
+ [0, 1, 0, 0],
+ [0, 0, 0, 1]])
+>>> # plot the confusion matrix
+>>> import matplotlib.pyplot as plt
+>>> y_score = torch.rand((1000, 10))
+>>> y_true = torch.randint(10, (1000, ))
+>>> matrix = ConfusionMatrix.calculate(y_score, y_true)
+>>> ConfusionMatrix().plot(matrix)
+>>> plt.show()
+```
+
+## **Use with Evalutor**
+
+```python
+>>> import torch
+>>> from mmpretrain.evaluation import ConfusionMatrix
+>>> from mmpretrain.structures import DataSample
+>>> from mmengine.evaluator import Evaluator
+>>> data_samples = [
+... DataSample().set_gt_label(i%5).set_pred_score(torch.rand(5))
+... for i in range(1000)
+... ]
+>>> evaluator = Evaluator(metrics=ConfusionMatrix())
+>>> evaluator.process(data_samples)
+>>> evaluator.evaluate(1000)
+{'confusion_matrix/result': tensor([[37, 37, 48, 43, 35],
+ [35, 51, 32, 46, 36],
+ [45, 28, 39, 42, 46],
+ [42, 40, 40, 35, 43],
+ [40, 39, 41, 37, 43]])}
+```
diff --git a/docs/zh_CN/index.rst b/docs/zh_CN/index.rst
index 7865da8eb..734dfc569 100644
--- a/docs/zh_CN/index.rst
+++ b/docs/zh_CN/index.rst
@@ -88,6 +88,7 @@ MMPretrain 上手路线
useful_tools/verify_dataset.md
useful_tools/log_result_analysis.md
useful_tools/complexity_analysis.md
+ useful_tools/confusion_matrix.md
.. toctree::
:maxdepth: 1
diff --git a/docs/zh_CN/useful_tools/confusion_matrix.md b/docs/zh_CN/useful_tools/confusion_matrix.md
new file mode 100644
index 000000000..98c039c63
--- /dev/null
+++ b/docs/zh_CN/useful_tools/confusion_matrix.md
@@ -0,0 +1,83 @@
+# 混淆矩阵
+
+MMPretrain 提供 `tools/analysis_tools/confusion_matrix.py` 工具来分析预测结果的混淆矩阵。关于混淆矩阵的介绍，可参考[链接](https://zh.wikipedia.org/zh-cn/%E6%B7%B7%E6%B7%86%E7%9F%A9%E9%98%B5)。
+
+## 命令行使用
+
+**命令行**：
+
+```shell
+python tools/analysis_tools/confusion_matrix.py \
+ ${CONFIG_FILE} \
+ ${CHECKPOINT} \
+ [--show] \
+ [--show-path] \
+ [--include-values] \
+ [--cmap ${CMAP}] \
+ [--cfg-options ${CFG-OPTIONS}]
+```
+
+**所有参数的说明**：
+
+- `config`：模型配置文件的路径。
+- `checkpoint`：权重路径。
+- `--show`：是否展示混淆矩阵的 matplotlib 可视化结果，默认不展示。
+- `--show-path`：如果 `show` 为 True，可视化结果的保存路径。
+- `--include-values`：是否在可视化结果上添加数值。
+- `--cmap`：可视化结果使用的颜色映射图，即 `cmap`，默认为 `viridis`。
+- `--cfg-options`：对配置文件的修改，参考[学习配置文件](../user_guides/config.md)。
+
+**使用示例**：
+
+```shell
+python tools/analysis_tools/confusion_matrix.py \
+ configs/resnet/resnet50_8xb16_cifar10.py \
+ https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth \
+ --show
+```
+
+**输出图片**：
+
+
+
+## 基础用法
+
+```python
+>>> import torch
+>>> from mmpretrain.evaluation import ConfusionMatrix
+>>> y_pred = [0, 1, 1, 3]
+>>> y_true = [0, 2, 1, 3]
+>>> ConfusionMatrix.calculate(y_pred, y_true, num_classes=4)
+tensor([[1, 0, 0, 0],
+ [0, 1, 0, 0],
+ [0, 1, 0, 0],
+ [0, 0, 0, 1]])
+>>> # plot the confusion matrix
+>>> import matplotlib.pyplot as plt
+>>> y_score = torch.rand((1000, 10))
+>>> y_true = torch.randint(10, (1000, ))
+>>> matrix = ConfusionMatrix.calculate(y_score, y_true)
+>>> ConfusionMatrix().plot(matrix)
+>>> plt.show()
+```
+
+## 结合评估器使用
+
+```python
+>>> import torch
+>>> from mmpretrain.evaluation import ConfusionMatrix
+>>> from mmpretrain.structures import DataSample
+>>> from mmengine.evaluator import Evaluator
+>>> data_samples = [
+... DataSample().set_gt_label(i%5).set_pred_score(torch.rand(5))
+... for i in range(1000)
+... ]
+>>> evaluator = Evaluator(metrics=ConfusionMatrix())
+>>> evaluator.process(data_samples)
+>>> evaluator.evaluate(1000)
+{'confusion_matrix/result': tensor([[37, 37, 48, 43, 35],
+ [35, 51, 32, 46, 36],
+ [45, 28, 39, 42, 46],
+ [42, 40, 40, 35, 43],
+ [40, 39, 41, 37, 43]])}
+```
