diff --git a/thirdparty/libsvm/HPKBUILD b/thirdparty/libsvm/HPKBUILD
new file mode 100644
index 0000000000000000000000000000000000000000..90a3aa11a3d98962b27d8b9c08bf2231df5739c1
--- /dev/null
+++ b/thirdparty/libsvm/HPKBUILD
@@ -0,0 +1,65 @@
+# Contributor: wupingyuan <wupingyuan@huawei.com>
+# Maintainer: wupingyuan <wupingyuan@huawei.com>
+pkgname="libsvm"
+pkgver="v331"
+pkgrel=0
+pkgdesc="Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification. This document explains the use of libsvm."
+url="https://github.com/cjlin1/libsvm"
+archs=("armeabi-v7a" "arm64-v8a")
+license=("BSD")
+depends=()
+makedepends=()
+install=
+source="https://github.com/cjlin1/$pkgname/archive/refs/tags/$pkgver.tar.gz"
+
+autounpack=true
+downloadpackage=true
+buildtools="make"
+builddir=$pkgname-${pkgver:1}
+packagename=$builddir.tar.gz
+cxx=
+
+prepare() {
+    cp -rf $builddir $builddir-$ARCH-build
+    if [ $ARCH == ${archs[0]} ]
+    then
+        cxx=${OHOS_SDK}/native/llvm/bin/arm-linux-ohos-clang++
+    fi
+    if [ $ARCH == ${archs[1]} ]
+    then
+        cxx=${OHOS_SDK}/native/llvm/bin/aarch64-linux-ohos-clang++
+    fi
+}
+
+build() {
+    cd $builddir-$ARCH-build
+    make CXX=${cxx} all lib -j4 > `pwd`/build.log 2>&1
+    ret=$?
+    cd $OLDPWD
+    return $ret
+}
+
+package() {
+    cd $builddir-$ARCH-build
+    # 由于没有install命令需要手动拷贝到安装目录
+    mkdir -p $LYCIUM_ROOT/usr/$pkgname-$ARCH-install/include/
+    mkdir -p $LYCIUM_ROOT/usr/$pkgname-$ARCH-install/lib/
+    cp svm.h $LYCIUM_ROOT/usr/$pkgname-$ARCH-install/include/
+    cp libsvm.so.3 $LYCIUM_ROOT/usr/$pkgname-$ARCH-install/lib/
+    ret=$?
+    cd $OLDPWD
+    unset cxx
+    return $ret
+}
+
+check() {
+    echo "The test must be on an OpenHarmony device!"
+    # 在OpenHarmony开发板中执行用例
+    # ./svm-scale  test.data  >  test.scale
+    # ./svm-train  test.scale  test.model
+    # ./svm-predict  test.scale  test.model  test.predict
+}
+
+cleanbuild(){
+    rm -rf ${PWD}/$builddir $builddir-armeabi-v7a-build  $builddir-arm64-v8a-build #${PWD}/$packagename
+}
\ No newline at end of file
diff --git a/thirdparty/libsvm/README.OpenSource b/thirdparty/libsvm/README.OpenSource
new file mode 100644
index 0000000000000000000000000000000000000000..069297350b25ccc3cc646c9909fa211519917a3f
--- /dev/null
+++ b/thirdparty/libsvm/README.OpenSource
@@ -0,0 +1,11 @@
+[
+    {
+        "Name": "libsvm",
+        "License": "BSD",
+        "License File": "COPYRIGHT",
+        "Version Number": "v331",
+        "Owner": "wupingyuan@huawei.com",
+        "Upstream URL": "https://github.com/cjlin1/libsvm/archive/refs/tags/v331.tar.gz",
+        "Description": "Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification. This document explains the use of libsvm."
+    }
+]
diff --git a/thirdparty/libsvm/README_zh.md b/thirdparty/libsvm/README_zh.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed0f3374e4350382cc1a64deac577461957e1b3a
--- /dev/null
+++ b/thirdparty/libsvm/README_zh.md
@@ -0,0 +1,12 @@
+# libsvm三方库说明
+## 功能简介
+libsvm是一个支持向量机的库。
+## 使用约束
+- IDE版本：DevEco Studio 3.1 Release
+- SDK版本：ohos_sdk_public 3.2.11.9 (API Version 9 Release)
+- 三方库版本：v331
+- 当前适配的功能：支持向量机
+- [BSD](https://github.com/cjlin1/libsvm)
+
+## 集成方式
++ [应用hap包集成](docs/hap_integrate.md)
diff --git a/thirdparty/libsvm/SHA512SUM b/thirdparty/libsvm/SHA512SUM
new file mode 100644
index 0000000000000000000000000000000000000000..8af706ac281c619a94e6379d82674f8aefc3f653
--- /dev/null
+++ b/thirdparty/libsvm/SHA512SUM
@@ -0,0 +1 @@
+78d3241fbf0286da077b8e612604feee13946b551888dcd8f47d23b4791f2a1daf516f3871e9a02d1b70cb58e12705603af8ef3c2ff0aac4211423d5053c7083  libsvm-331.tar.gz
\ No newline at end of file
diff --git a/thirdparty/libsvm/docs/hap_integrate.md b/thirdparty/libsvm/docs/hap_integrate.md
new file mode 100644
index 0000000000000000000000000000000000000000..b4f010868094f9496020b524b82a290ada91bab9
--- /dev/null
+++ b/thirdparty/libsvm/docs/hap_integrate.md
@@ -0,0 +1,90 @@
+# libsvm集成到应用hap
+本库是在RK3568开发板上基于OpenHarmony3.2 Release版本的镜像验证的，如果是从未使用过RK3568，可以先查看[润和RK3568开发板标准系统快速上手](https://gitee.com/openharmony-sig/knowledge_demo_temp/tree/master/docs/rk3568_helloworld)。
+## 开发环境
+- ubuntu20.04
+- [OpenHarmony3.2Release镜像](https://gitee.com/link?target=https%3A%2F%2Frepo.huaweicloud.com%2Fopenharmony%2Fos%2F3.2-Release%2Fdayu200_standard_arm32.tar.gz)
+- [ohos_sdk_public 3.2.11.9 (API Version 9 Release)](https://gitee.com/link?target=https%3A%2F%2Frepo.huaweicloud.com%2Fopenharmony%2Fos%2F3.2-Release%2Fohos-sdk-windows_linux-public.tar.gz)
+- [DevEco Studio 3.1 Beta2](https://gitee.com/link?target=https%3A%2F%2Fcontentcenter-vali-drcn.dbankcdn.cn%2Fpvt_2%2FDeveloperAlliance_package_901_9%2Ff3%2Fv3%2FuJyuq3syQ2ak4hE1QZmAug%2Fdevecostudio-windows-3.1.0.400.zip%3FHW-CC-KV%3DV1%26HW-CC-Date%3D20230408T013335Z%26HW-CC-Expire%3D315360000%26HW-CC-Sign%3D96262721EDC9B34E6F62E66884AB7AE2A94C2A7B8C28D6F7FC891F46EB211A70)
+- [准备三方库构建环境](../../../tools/README.md#编译环境准备)
+- [准备三方库测试环境](../../../tools/README.md#ci环境准备)
+## 编译三方库
+- 下载本仓库
+  ```
+  git clone https://gitee.com/openharmony-sig/tpc_c_cplusplus.git --depth=1
+  ```
+- 三方库目录结构
+  ```
+  tpc_c_cplusplus/thirdparty/libsvm  #三方库libsvm的目录结构如下
+  ├── docs                              #三方库相关文档的文件夹
+  ├── HPKBUILD                          #构建脚本
+  ├── SHA512SUM                         #三方库校验文件
+  ├── README.OpenSource                 #说明三方库源码的下载地址，版本，license等信息
+  ├── README_zh.md   
+  ```
+  
+- 将libsvm拷贝至tools/main目录下
+  ```
+  cd tpc_c_cplusplus
+  cp thirdparty/libsvm tools/main -rf
+  ```
+- 在tools目录下编译三方库
+  编译环境的搭建参考[准备三方库构建环境](../../../tools/README.md#编译环境准备)
+  ```
+  cd tools
+  ./build.sh libsvm
+  ```
+- 三方库头文件及生成的库
+  在tools目录下会生成usr目录，该目录下存在已编译完成的32位和64位三方库
+  ```
+  libsvm-arm64-v8a-install   libsvm-armeabi-v7a-install
+  ```
+
+- [测试三方库](#测试三方库)
+
+## 应用中使用三方库
+
+- 在IDE的cpp目录下新增thirdparty目录，将编译生成的库拷贝到该目录下，如下图所示
+&nbsp;![thirdparty_install_dir](pic/libsvm_install_dir.jpg)
+- 在最外层（cpp目录下）CMakeLists.txt中添加如下语句
+  ```
+  #将三方库加入工程中
+  add_library(svm SHARED IMPORTED)
+  set_target_properties(svm
+        PROPERTIES IMPORTED_LOCATION
+        ${CMAKE_SOURCE_DIR}/../../../libs/${OHOS_ARCH}/libsvm.so)
+  target_link_libraries(entry PUBLIC libace_napi.z.so svm)        
+  #将三方库的头文件加入工程中
+  target_include_directories(entry PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/libsvm-${OHOS_ARCH}-install/include)
+  ```
+  ![libsvm_usage](pic/libsvm_usage.jpg)
+## 测试三方库
+三方库的测试使用原库自带的测试用例来做测试，[准备三方库测试环境](../../../tools/README.md#ci环境准备)
+
+- 将编译生成的svm-train、svm-scale、svm-predict可执行文件及音频测试文件test.data准备好
+
+- 将准备好的文件推送到开发板，在windows命令行进行如下操作
+
+  ```
+  hdc_std shell mount -o remount,rw /     #修改系统权限为可读写
+  hdc_std file send svm-train /data         #将可执行文件推入开发板data目录
+  hdc_std file send svm-scale /data
+  hdc_std file send svm-predict /data
+  hdc_std file send libc++_shared.so /system/lib64 
+  hdc_std file send libsvm.so /system/lib64
+  hdc_std file send test.data /data  #将测试文件推入开发板data目录
+  hdc_std shell                          #进入开 发板
+  chmod 777 svm-train              #添加权限
+  chmod 777 svm-scale              #添加权限
+  chmod 777 svm-predict             #添加权限
+  ./svm-scale  /data/test.data  >  /data/test.scale # 执行测试用例
+  ./svm-train  /data/test.scale  /data/test.model
+  ./svm-predict  /data/test.scale  /data/test.model  /data/test.predict
+  ```
+
+&nbsp;![libsvm_test](pic/libsvm_test.jpg)
+
+## 参考资料
+- [润和RK3568开发板标准系统快速上手](https://gitee.com/openharmony-sig/knowledge_demo_temp/tree/master/docs/rk3568_helloworld)
+- [OpenHarmony三方库地址](https://gitee.com/openharmony-tpc)
+- [OpenHarmony知识体系](https://gitee.com/openharmony-sig/knowledge)
+- [通过DevEco Studio开发一个NAPI工程](https://gitee.com/openharmony-sig/knowledge_demo_temp/blob/master/docs/napi_study/docs/hello_napi.md)
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