diff --git a/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md b/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md
index 71b97aa996..087d6540e6 100644
--- a/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md
+++ b/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md
@@ -91,3 +91,18 @@ taos> select _flow, _fhigh, _frowts, forecast(i32) from foo;
 ## 内置预测算法
 - [arima](./02-arima.md)
 - [holtwinters](./03-holtwinters.md)
+- CES (Complex Exponential Smoothing) 
+- Theta
+- Prophet
+- XGBoost
+- LightGBM
+- Multiple Seasonal-Trend decomposition using LOESS (MSTL)
+- ETS (Error, Trend, Seasonal)
+- Long Short-Term Memory (LSTM)
+- Multilayer Perceptron (MLP)
+- DeepAR
+- N-BEATS
+- N-HiTS
+- PatchTST (Patch Time Series Transformer)
+- Temporal Fusion Transformer
+- TimesNet
diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md
index d0d6815c25..4055d42572 100644
--- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md
+++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md
@@ -50,6 +50,13 @@ FROM foo
 ANOMALY_WINDOW(foo.i32, "algo=shesd,direction=both,anoms=0.05")
 ```
 
+后续待添加异常检测算法
+- Gaussian Process Regression
+
+基于变点检测的异常检测算法  
+- CUSUM (Cumulative Sum Control Chart)
+- PELT (Pruned Exact Linear Time)
+
 ### 参考文献
 1. [https://en.wikipedia.org/wiki/68–95–99.7 rule](https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule)
 2. https://en.wikipedia.org/wiki/Interquartile_range
diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md
index 7c0998c917..00cd7aaa61 100644
--- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md
+++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md
@@ -3,7 +3,7 @@ title: "数据密度算法"
 sidebar_label: "数据密度算法"
 ---
 
-### 基于数据密度的检测方法
+### 基于数据密度/数据挖掘的检测算法
 LOF<sup>[1]</sup>: Local Outlier Factor(LOF)，局部离群因子/局部异常因子，
 是 Breunig 在 2000 年提出的一种基于密度的局部离群点检测算法，该方法适用于不同类簇密度分散情况迥异的数据。根据数据点周围的数据密集情况，首先计算每个数据点的一个局部可达密度，然后通过局部可达密度进一步计算得到每个数据点的一个离群因子，
 该离群因子即标识了一个数据点的离群程度，因子值越大，表示离群程度越高，因子值越小，表示离群程度越低。最后，输出离群程度最大的 $topK$ 个点。
@@ -15,6 +15,14 @@ FROM foo
 ANOMALY_WINDOW(foo.i32, "algo=lof")
 ```
 
+后续待添加基于数据挖掘检测算法
+- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
+- K-Nearest Neighbors (KNN)
+- Principal Component Analysis (PCA)
+
+第三方异常检测算法库
+- PyOD
+
 ### 参考文献
 
 1. Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4.
diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md
index ec76d6a0a3..b752d446eb 100644
--- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md
+++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md
@@ -12,6 +12,11 @@ FROM foo
 ANOMALY_WINDOW(col1, 'algo=encoder, model=ad_autoencoder_foo');
 ```
 
+后续添加机器（深度）学习异常检测算法
+- Isolation Forest
+- One-Class Support Vector Machines (SVM)
+- Prophet
+
 ### 参考文献
 
 1. https://en.wikipedia.org/wiki/Autoencoder
diff --git a/docs/zh/06-advanced/06-TDgpt/07-faq.md b/docs/zh/06-advanced/06-TDgpt/07-faq.md
new file mode 100644
index 0000000000..a8338265bc
--- /dev/null
+++ b/docs/zh/06-advanced/06-TDgpt/07-faq.md
@@ -0,0 +1,24 @@
+---
+title: "常见问题"
+sidebar_label: "常见问题"
+---
+
+1. 创建 anode 失败
+
+```bash
+taos> create anode '127.0.0.1:6090';
+
+DB error: Analysis service can't access[0x80000441] (0.117446s)
+```
+
+请检查 anode 服务是否工作正常。
+
+```bash
+curl '127.0.0.1:6090'
+curl: (7) Failed to connect to 127.0.0.1 port 6090: Connection refused
+```
+
+```bash
+TDengine© Time Series Data Analytics Platform (ver 1.0.x)
+```
+