diff --git a/sources/onnxruntime/quick_start.rst b/sources/onnxruntime/quick_start.rst
index 60cf448..39a83ca 100644
--- a/sources/onnxruntime/quick_start.rst
+++ b/sources/onnxruntime/quick_start.rst
@@ -35,6 +35,8 @@ ONNX Runtime 推理需要 ONNX 格式模型作为输入，目前有以下几种
 模型推理
 -----------
 
+python推理示例
+~~~~~~~~~~~~~~~~~~~~
 .. code-block:: python
   :linenos:
 
@@ -95,3 +97,148 @@ ONNX Runtime 推理需要 ONNX 格式模型作为输入，目前有以下几种
       img = preprocess(image_path)
       result = inference(model_path, img)
       display(classes_path, result)
+
+C++推理示例
+~~~~~~~~~~~~~~~~~
+.. code-block:: c++
+  :linenos:
+
+    #include <iostream>
+    #include <vector>
+
+    #include "onnxruntime_cxx_api.h"
+
+    // path of model, Change to user's own model path
+    const char* model_path = "./onnx/resnet50_Opset16.onnx";
+
+    /**
+    * @brief Input data preparation provided by user.
+    *
+    * @param num_input_nodes The number of model input nodes.
+    * @return  A collection of input data.
+    */
+    std::vector<std::vector<float>> input_prepare(size_t num_input_nodes) {
+        std::vector<std::vector<float>> input_datas;
+        input_datas.reserve(num_input_nodes);
+
+        constexpr size_t input_data_size = 3 * 224 * 224;
+        std::vector<float> input_data(input_data_size);
+        // initialize input data with values in [0.0, 1.0]
+        for (unsigned int i = 0; i < input_data_size; i++)
+            input_data[i] = (float)i / (input_data_size + 1);
+        input_datas.push_back(input_data);
+
+        return input_datas;
+    }
+
+    /**
+    * @brief Model output data processing logic(For User updates).
+    *
+    * @param output_tensors The results of the model output.
+    */
+    void output_postprocess(std::vector<Ort::Value>& output_tensors) {
+        auto floatarr = output_tensors.front().GetTensorMutableData<float>();
+
+        for (int i = 0; i < 5; i++) {
+            std::cout << "Score for class [" << i << "] =  " << floatarr[i] << '\n';
+        }
+        
+        std::cout << "Done!" << std::endl;
+    }
+
+    /**
+    * @brief The main functions for model inference.
+    *
+    *  The complete model inference process, which generally does not need to be
+    * changed here
+    */
+    void inference() {
+        const auto& api = Ort::GetApi();
+        Ort::Env env(ORT_LOGGING_LEVEL_WARNING);
+
+        // Enable cann graph in cann provider option.
+        OrtCANNProviderOptions* cann_options = nullptr;
+        api.CreateCANNProviderOptions(&cann_options);
+
+        // Configurations of EP
+        std::vector<const char*> keys{
+            "device_id",
+            "npu_mem_limit",
+            "arena_extend_strategy",
+            "enable_cann_graph"};
+        std::vector<const char*> values{"0", "4294967296", "kNextPowerOfTwo", "1"};
+        api.UpdateCANNProviderOptions(
+            cann_options, keys.data(), values.data(), keys.size());
+
+        // Convert to general session options
+        Ort::SessionOptions session_options;
+        api.SessionOptionsAppendExecutionProvider_CANN(
+            static_cast<OrtSessionOptions*>(session_options), cann_options);
+
+        Ort::Session session(env, model_path, session_options);
+
+        Ort::AllocatorWithDefaultOptions allocator;
+
+        // Input Process
+        const size_t num_input_nodes = session.GetInputCount();
+        std::vector<const char*> input_node_names;
+        std::vector<Ort::AllocatedStringPtr> input_names_ptr;
+        input_node_names.reserve(num_input_nodes);
+        input_names_ptr.reserve(num_input_nodes);
+        std::vector<std::vector<int64_t>> input_node_shapes;
+        std::cout << num_input_nodes << std::endl;
+        for (size_t i = 0; i < num_input_nodes; i++) {
+            auto input_name = session.GetInputNameAllocated(i, allocator);
+            input_node_names.push_back(input_name.get());
+            input_names_ptr.push_back(std::move(input_name));
+            auto type_info = session.GetInputTypeInfo(i);
+            auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
+            input_node_shapes.push_back(tensor_info.GetShape());
+        }
+
+        // Output Process
+        const size_t num_output_nodes = session.GetOutputCount();
+        std::vector<const char*> output_node_names;
+        std::vector<Ort::AllocatedStringPtr> output_names_ptr;
+        output_names_ptr.reserve(num_input_nodes);
+        output_node_names.reserve(num_output_nodes);
+        for (size_t i = 0; i < num_output_nodes; i++) {
+            auto output_name = session.GetOutputNameAllocated(i, allocator);
+            output_node_names.push_back(output_name.get());
+            output_names_ptr.push_back(std::move(output_name));
+        }
+
+        //  User need to generate input date according to real situation.
+        std::vector<std::vector<float>> input_datas = input_prepare(num_input_nodes);
+
+        auto memory_info = Ort::MemoryInfo::CreateCpu(
+            OrtAllocatorType::OrtArenaAllocator, OrtMemTypeDefault);
+
+        std::vector<Ort::Value> input_tensors;
+        input_tensors.reserve(num_input_nodes);
+        for (size_t i = 0; i < input_node_shapes.size(); i++) {
+            auto input_tensor = Ort::Value::CreateTensor<float>(
+                memory_info,
+                input_datas[i].data(),
+                input_datas[i].size(),
+                input_node_shapes[i].data(),
+                input_node_shapes[i].size());
+            input_tensors.push_back(std::move(input_tensor));
+        }
+
+        auto output_tensors = session.Run(
+            Ort::RunOptions{nullptr},
+            input_node_names.data(),
+            input_tensors.data(),
+            num_input_nodes,
+            output_node_names.data(),
+            output_node_names.size());
+
+        // Processing of out_tensor
+        output_postprocess(output_tensors);
+    }
+
+    int main(int argc, char* argv[]) {
+        inference();
+        return 0;
+    }
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