diff --git a/src/summeryanyfile/core/chunkers/semantic_chunker.py b/src/summeryanyfile/core/chunkers/semantic_chunker.py
new file mode 100644
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+++ b/src/summeryanyfile/core/chunkers/semantic_chunker.py
@@ -0,0 +1,385 @@
+"""
+语义分块器 - 基于Markdown头部结构的智能分块
+"""
+
+import re
+import logging
+from typing import List, Dict, Any, Optional, Tuple
+
+from .base_chunker import BaseChunker, DocumentChunk
+
+logger = logging.getLogger(__name__)
+
+
+class SemanticChunker(BaseChunker):
+    """
+    语义分块器，使用Markdown头部结构创建有意义的块
+    
+    这是主要推荐的分块策略，因为它保留了文档结构和语义边界
+    """
+    
+    def __init__(
+        self, 
+        chunk_size: int = 1000,
+        chunk_overlap: int = 200,
+        headers_to_split_on: Optional[List[Tuple[str, str]]] = None
+    ) -> None:
+        """
+        初始化语义分块器
+        
+        Args:
+            chunk_size: 每个块的最大大小
+            chunk_overlap: 块之间的重叠
+            headers_to_split_on: (头部模式, 头部名称) 元组列表
+        """
+        super().__init__(chunk_size, chunk_overlap)
+        
+        if headers_to_split_on is None:
+            self.headers_to_split_on = [
+                ("#", "Chapter"),
+                ("##", "Section"),
+                ("###", "Subsection"),
+                ("####", "Subsubsection"),
+            ]
+        else:
+            self.headers_to_split_on = headers_to_split_on
+    
+    def chunk_text(self, text: str, metadata: Optional[Dict[str, Any]] = None) -> List[DocumentChunk]:
+        """
+        使用Markdown头部结构和自定义模式分块文本
+
+        Args:
+            text: 输入文本（最好是Markdown格式）
+            metadata: 可选的元数据
+
+        Returns:
+            DocumentChunk对象列表
+        """
+        if metadata is None:
+            metadata = {}
+
+        try:
+            # 首先尝试标准Markdown头部
+            chunks = self._chunk_by_markdown_headers(text, metadata)
+
+            # 如果只得到一个块，尝试自定义模式
+            if len(chunks) <= 1:
+                logger.info("未找到标准Markdown头部，尝试自定义模式")
+                return self._chunk_with_custom_patterns(text, metadata)
+
+            # 验证块大小并在必要时分割
+            final_chunks = []
+            for i, chunk in enumerate(chunks):
+                if not self.validate_chunk_size(chunk):
+                    logger.warning(f"块 {i} 超过大小限制，应用递归分割")
+                    sub_chunks = self._split_large_chunk(chunk)
+                    final_chunks.extend(sub_chunks)
+                else:
+                    final_chunks.append(chunk)
+
+            logger.info(f"创建了 {len(final_chunks)} 个语义块")
+            return final_chunks
+
+        except Exception as e:
+            logger.error(f"语义分块错误: {e}")
+            # 回退到简单段落分割
+            return self._fallback_chunking(text, metadata)
+    
+    def _chunk_by_markdown_headers(self, text: str, metadata: Dict[str, Any]) -> List[DocumentChunk]:
+        """
+        基于Markdown头部分块
+        
+        Args:
+            text: 输入文本
+            metadata: 基础元数据
+            
+        Returns:
+            DocumentChunk对象列表
+        """
+        lines = text.split('\n')
+        chunks = []
+        current_chunk_lines = []
+        current_headers = {}
+        chunk_index = 0
+        
+        for line_num, line in enumerate(lines, 1):
+            line_stripped = line.strip()
+            
+            # 检查是否是头部
+            header_info = self._detect_header(line_stripped)
+            
+            if header_info:
+                # 保存当前块（如果有内容）
+                if current_chunk_lines:
+                    chunk_content = '\n'.join(current_chunk_lines).strip()
+                    if chunk_content:
+                        chunk_metadata = metadata.copy()
+                        chunk_metadata.update({
+                            "chunk_index": chunk_index,
+                            "chunking_strategy": "semantic",
+                            "headers": current_headers.copy(),
+                            "start_line": line_num - len(current_chunk_lines),
+                            "end_line": line_num - 1
+                        })
+                        chunks.append(self._create_chunk(chunk_content, chunk_metadata))
+                        chunk_index += 1
+                
+                # 更新当前头部信息
+                header_level, header_name = header_info
+                current_headers[header_name] = line_stripped[header_level:].strip()
+                
+                # 清除更深层级的头部
+                levels_to_remove = []
+                for existing_level, existing_name in self.headers_to_split_on:
+                    if len(existing_level) > header_level:
+                        levels_to_remove.append(existing_name)
+                
+                for level_name in levels_to_remove:
+                    current_headers.pop(level_name, None)
+                
+                # 开始新块
+                current_chunk_lines = [line]
+            else:
+                current_chunk_lines.append(line)
+        
+        # 添加最后一个块
+        if current_chunk_lines:
+            chunk_content = '\n'.join(current_chunk_lines).strip()
+            if chunk_content:
+                chunk_metadata = metadata.copy()
+                chunk_metadata.update({
+                    "chunk_index": chunk_index,
+                    "chunking_strategy": "semantic",
+                    "headers": current_headers.copy(),
+                    "start_line": len(lines) - len(current_chunk_lines) + 1,
+                    "end_line": len(lines)
+                })
+                chunks.append(self._create_chunk(chunk_content, chunk_metadata))
+        
+        return chunks
+    
+    def _detect_header(self, line: str) -> Optional[Tuple[int, str]]:
+        """
+        检测是否为头部行
+        
+        Args:
+            line: 要检测的行
+            
+        Returns:
+            (头部级别, 头部名称) 或 None
+        """
+        for header_pattern, header_name in self.headers_to_split_on:
+            if line.startswith(header_pattern + ' '):
+                return len(header_pattern), header_name
+        return None
+
+    def _split_large_chunk(self, chunk: DocumentChunk) -> List[DocumentChunk]:
+        """
+        分割大块使用递归字符分割
+
+        Args:
+            chunk: 要分割的大块
+
+        Returns:
+            较小块的列表
+        """
+        # 导入递归分块器作为回退
+        from .recursive_chunker import RecursiveChunker
+
+        recursive_chunker = RecursiveChunker(
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap
+        )
+
+        sub_chunks = recursive_chunker.chunk_text(chunk.content, chunk.metadata)
+
+        # 更新元数据以表明这是从语义块分割的
+        for i, sub_chunk in enumerate(sub_chunks):
+            sub_chunk.metadata["parent_chunk_id"] = chunk.chunk_id
+            sub_chunk.metadata["sub_chunk_index"] = i
+            sub_chunk.metadata["chunking_strategy"] = "semantic_recursive"
+
+        return sub_chunks
+
+    def _chunk_with_custom_patterns(self, text: str, metadata: Dict[str, Any]) -> List[DocumentChunk]:
+        """
+        当未找到标准Markdown头部时使用自定义模式分块文本
+
+        Args:
+            text: 输入文本
+            metadata: 基础元数据
+
+        Returns:
+            DocumentChunk对象列表
+        """
+        logger.info("使用基于自定义模式的分块")
+
+        # 尝试查找可能表示结构的自定义模式
+        custom_patterns = [
+            r'^[A-Z][^.!?]*[.!?]\s*$',  # 可能是标题的句子
+            r'^\d+\.\s+',  # 编号列表
+            r'^[A-Z\s]+:',  # 带冒号的全大写标签
+            r'^[*-]\s+',   # 项目符号
+            r'^\w+\s*\([^)]*\)',  # 带括号的文本
+        ]
+
+        lines = text.split('\n')
+        potential_breaks = []
+
+        for i, line in enumerate(lines):
+            line = line.strip()
+            if not line:
+                continue
+
+            # 检查行是否匹配任何自定义模式
+            for pattern in custom_patterns:
+                if re.match(pattern, line):
+                    potential_breaks.append(i)
+                    break
+
+        # 如果找到潜在断点，使用它们
+        if potential_breaks:
+            return self._chunk_by_breaks(text, potential_breaks, metadata)
+        else:
+            # 回退到基于段落的分块
+            return self._fallback_chunking(text, metadata)
+
+    def _chunk_by_breaks(self, text: str, break_points: List[int], metadata: Dict[str, Any]) -> List[DocumentChunk]:
+        """
+        使用识别的断点分块文本
+
+        Args:
+            text: 输入文本
+            break_points: 应该发生断点的行号
+            metadata: 基础元数据
+
+        Returns:
+            DocumentChunk对象列表
+        """
+        lines = text.split('\n')
+        chunks = []
+        current_chunk_lines = []
+        chunk_index = 0
+
+        for i, line in enumerate(lines):
+            current_chunk_lines.append(line)
+
+            # 检查这是否是断点且我们有内容
+            if i in break_points and len(current_chunk_lines) > 1:
+                # 从累积的行创建块（排除当前行）
+                chunk_content = '\n'.join(current_chunk_lines[:-1]).strip()
+                if chunk_content:
+                    chunk_metadata = metadata.copy()
+                    chunk_metadata.update({
+                        "chunk_index": chunk_index,
+                        "chunking_strategy": "custom_pattern"
+                    })
+                    chunks.append(self._create_chunk(chunk_content, chunk_metadata))
+                    chunk_index += 1
+
+                # 用当前行开始新块
+                current_chunk_lines = [line]
+
+        # 如果有剩余内容，添加最后一个块
+        if current_chunk_lines:
+            chunk_content = '\n'.join(current_chunk_lines).strip()
+            if chunk_content:
+                chunk_metadata = metadata.copy()
+                chunk_metadata.update({
+                    "chunk_index": chunk_index,
+                    "chunking_strategy": "custom_pattern"
+                })
+                chunks.append(self._create_chunk(chunk_content, chunk_metadata))
+
+        return chunks
+
+    def _fallback_chunking(self, text: str, metadata: Dict[str, Any]) -> List[DocumentChunk]:
+        """
+        当Markdown解析失败时的回退分块方法
+
+        Args:
+            text: 输入文本
+            metadata: 基础元数据
+
+        Returns:
+            DocumentChunk对象列表
+        """
+        logger.warning("使用基于段落的回退分块")
+
+        # 按双换行符分割（段落）
+        paragraphs = text.split('\n\n')
+        chunks = []
+        current_chunk = ""
+        chunk_index = 0
+
+        for paragraph in paragraphs:
+            paragraph = paragraph.strip()
+            if not paragraph:
+                continue
+
+            # 检查添加此段落是否会超过块大小
+            potential_chunk = current_chunk + "\n\n" + paragraph if current_chunk else paragraph
+
+            if len(potential_chunk) <= self.chunk_size:
+                current_chunk = potential_chunk
+            else:
+                # 如果有内容，保存当前块
+                if current_chunk:
+                    chunk_metadata = metadata.copy()
+                    chunk_metadata.update({
+                        "chunk_index": chunk_index,
+                        "chunking_strategy": "fallback_paragraph"
+                    })
+                    chunks.append(self._create_chunk(current_chunk, chunk_metadata))
+                    chunk_index += 1
+
+                # 用当前段落开始新块
+                current_chunk = paragraph
+
+        # 添加最后一个块
+        if current_chunk:
+            chunk_metadata = metadata.copy()
+            chunk_metadata.update({
+                "chunk_index": chunk_index,
+                "chunking_strategy": "fallback_paragraph"
+            })
+            chunks.append(self._create_chunk(current_chunk, chunk_metadata))
+
+        return chunks
+
+    def extract_document_structure(self, text: str) -> Dict[str, Any]:
+        """
+        提取文档结构信息
+
+        Args:
+            text: 输入文本
+
+        Returns:
+            包含结构信息的字典
+        """
+        structure = {
+            "headers": [],
+            "total_sections": 0,
+            "max_depth": 0
+        }
+
+        lines = text.split('\n')
+        for line_num, line in enumerate(lines, 1):
+            line = line.strip()
+            for header_pattern, header_name in self.headers_to_split_on:
+                if line.startswith(header_pattern + ' '):
+                    header_text = line[len(header_pattern):].strip()
+                    depth = len(header_pattern)
+
+                    structure["headers"].append({
+                        "text": header_text,
+                        "level": header_name,
+                        "depth": depth,
+                        "line_number": line_num
+                    })
+
+                    structure["max_depth"] = max(structure["max_depth"], depth)
+                    break
+
+        structure["total_sections"] = len(structure["headers"])
+        return structure