diff --git a/rfcs/APIs/20230925_api_design_for_SubsetRandomSampler.md b/rfcs/APIs/20230925_api_design_for_SubsetRandomSampler.md
new file mode 100644
index 000000000..0007e69f0
--- /dev/null
+++ b/rfcs/APIs/20230925_api_design_for_SubsetRandomSampler.md
@@ -0,0 +1,138 @@
+# paddle.utils.data.SubsetRandomSampler API 增强设计文档
+
+| API名称      | paddle.utils.data.SubsetRandomSampler|
+| ------------ | -------------------------------------- |
+| 提交作者     | Asthestarsfalll                                    |
+| 提交时间     | 2023-09-25                             |
+| 版本号       | V1.0                                   |
+| 依赖飞桨版本  |  develop                                |
+| 文件名       | 20230925_api_design_for_SubsetRandomSampler.md |
+
+
+ # 一、概述
+ ## 1、相关背景
+
+`SubsetRandomSampler`子集随机采样器，支持从数据集的指定子集中随机选择样本，可以用于将数据集分成训练集和验证集等子集。
+
+ ## 2、功能目标
+
+增加`paddle.utils.data.SubsetRandomSampler`，实现对给定子集的随机采样。
+
+ ## 3、意义
+
+飞桨支持`SubsetRandomSampler`。
+
+ # 二、飞桨现状
+
+目前paddle缺少相关功能实现，但是有类似功能的API，只需要继承`Sampler` 基类，并重写`__iter__`和`__iter__`方法实现相关功能即可。
+
+
+ # 三、业内方案调研
+
+ ## PyTorch
+
+Pytorch中有API`torch.utils.data.SubsetRandomSampler(indices, generator)`.在pytorch中，介绍为：
+
+```
+Samples elements randomly from a given list of indices, without replacement.
+```
+
+ ### 实现方法
+
+实现方法较为简单，其子集通过给定的`indices`确定，采样时只需要从`indices`中采样便可达到相应的效果。
+
+```python
+class SubsetRandomSampler(Sampler[int]):
+    r"""Samples elements randomly from a given list of indices, without replacement.
+
+    Args:
+        indices (sequence): a sequence of indices
+        generator (Generator): Generator used in sampling.
+    """
+    indices: Sequence[int]
+
+    def __init__(self, indices: Sequence[int], generator=None) -> None:
+        self.indices = indices
+        self.generator = generator
+
+    def __iter__(self) -> Iterator[int]:
+        for i in torch.randperm(len(self.indices), generator=self.generator):
+            yield self.indices[i]
+
+    def __len__(self) -> int:
+        return len(self.indices)
+
+```
+
+ ## MindSpore
+
+MindSpore 中`Sampler`的整体设计与`Paddle`并不相同
+
+ ``` python
+class SubsetRandomSampler(SubsetSampler):
+    """
+    Samples the elements randomly from a sequence of indices.
+
+    Args:
+        indices (Iterable): A sequence of indices (Any iterable Python object but string).
+        num_samples (int, optional): Number of elements to sample. Default: ``None`` , which means sample all elements.
+
+    Raises:
+        TypeError: If elements of `indices` are not of type number.
+        TypeError: If `num_samples` is not of type int.
+        ValueError: If `num_samples` is a negative value.
+
+    Examples:
+        >>> import mindspore.dataset as ds
+        >>> indices = [0, 1, 2, 3, 7, 88, 119]
+        >>>
+        >>> # create a SubsetRandomSampler, will sample from the provided indices
+        >>> sampler = ds.SubsetRandomSampler(indices)
+        >>> data = ds.ImageFolderDataset(image_folder_dataset_dir, num_parallel_workers=8, sampler=sampler)
+    """
+
+    def parse(self):
+        """ Parse the sampler."""
+        num_samples = self.num_samples if self.num_samples is not None else 0
+        c_sampler = cde.SubsetRandomSamplerObj(self.indices, num_samples)
+        c_child_sampler = self.parse_child()
+        c_sampler.add_child(c_child_sampler)
+        return c_sampler
+
+    def is_shuffled(self):
+        return True
+
+    def parse_for_minddataset(self):
+        """Parse the sampler for MindRecord."""
+        c_sampler = cde.MindrecordSubsetSampler(self.indices, ds.config.get_seed())
+        c_child_sampler = self.parse_child_for_minddataset()
+        c_sampler.add_child(c_child_sampler)
+        c_sampler.set_num_samples(self.get_num_samples())
+        return c_sampler
+ ```
+
+ ## API实现方案
+
+pytorch的sampler整体设计与paddle类似，因此考虑参考pytorch的方案实现
+在 python\paddle\io\sampler.py 中添加对应类。但是由于paddle并没有完全支持`generator`，因此将该参数移除。
+
+ # 六、测试和验收的考量
+
+ 测试考虑的 case 如下：
+ - 确保结果符合预期:一次遍历中遍历所有的 `index` 一次且仅有一次，确保不重复不遗漏.
+
+ # 七、可行性分析和排期规划
+
+ 方案实施难度可控，工期上可以满足在当前版本周期内开发完成。
+
+ # 八、影响面
+
+ 为已有 API 的增强，对其他模块没有影响
+
+ # 名词解释
+
+ 无
+
+ # 附件及参考资料
+
+ 无