diff --git a/tools/notion-corpus-loader/src/structure.py b/tools/notion-corpus-loader/src/structure.py
new file mode 100644
index 0000000..8d65848
--- /dev/null
+++ b/tools/notion-corpus-loader/src/structure.py
@@ -0,0 +1,88 @@
+# 结构化语料数据模型
+# HLDP://tools/notion-corpus-loader/structure
+
+from dataclasses import dataclass, field
+from typing import List, Dict, Optional
+import json
+
+@dataclass
+class StructuredCorpusItem:
+    source_id: str = ""
+    source_type: str = ""
+    hldp_path: str = ""
+    title: str = ""
+    blocks: List[Dict] = field(default_factory=list)
+    raw_text: str = ""
+    cognitive_jumps: List[str] = field(default_factory=list)
+    causal_chains: List[str] = field(default_factory=list)
+    persona: str = ""
+    quality_score: float = 0.5
+    quality_tags: List[str] = field(default_factory=list)
+    created_time: str = ""
+    last_edited_time: str = ""
+    metadata: Dict = field(default_factory=dict)
+    
+    def to_training_sample(self, format: str = "text") -> str:
+        if format == "text": return self._to_text_sample()
+        elif format == "chat": return self._to_chat_sample()
+        return self._to_text_sample()
+    
+    def _to_text_sample(self) -> str:
+        parts = []
+        if self.hldp_path: parts.append(f"[HLDP_PATH]{self.hldp_path}[/HLDP_PATH]")
+        if self.persona: parts.append(f"[PERSONA]{self.persona}[/PERSONA]")
+        if self.title: parts.append(f"[TITLE]{self.title}[/TITLE]")
+        for jump in self.cognitive_jumps[:3]: parts.append(f"[COGNITIVE_JUMP]{jump}[/COGNITIVE_JUMP]")
+        for chain in self.causal_chains[:3]: parts.append(f"[CAUSAL_CHAIN]{chain}[/CAUSAL_CHAIN]")
+        if self.quality_score > 0.7: parts.append("[QUALITY_HIGH]")
+        elif self.quality_score > 0.4: parts.append("[QUALITY_MEDIUM]")
+        parts.append("[CONTENT]")
+        for block in self.blocks[:50]:
+            bt = block.get('type','paragraph'); c = block.get('content','')
+            if c: parts.append(f"[{bt.upper()}]{c}[/{bt.upper()}]")
+        parts.append("[/CONTENT]")
+        return '\n'.join(parts)
+    
+    def _to_chat_sample(self) -> str:
+        parts = []
+        if self.persona: parts.append(f"[PERSONA]{self.persona}[/PERSONA]")
+        parts.append("[THINKING]")
+        for block in self.blocks[:30]:
+            c = block.get('content','')
+            if c: parts.append(c[:200])
+        parts.append("[/THINKING]")
+        return '\n'.join(parts)
+    
+    def estimate_tokens(self) -> int:
+        cc = sum(1 for c in self.raw_text if '\u4e00' <= c <= '\u9fff')
+        tc = len(self.raw_text)
+        return int(cc * 1.5 + (tc - cc) * 0.75)
+
+@dataclass
+class CorpusCollection:
+    items: List[StructuredCorpusItem] = field(default_factory=list)
+    total_tokens: int = 0
+    persona_distribution: Dict[str, int] = field(default_factory=dict)
+    
+    def add(self, item: StructuredCorpusItem):
+        self.items.append(item)
+        t = item.estimate_tokens()
+        self.total_tokens += t
+        p = item.persona or "unknown"
+        self.persona_distribution[p] = self.persona_distribution.get(p,0) + t
+    
+    def summary(self) -> str:
+        total = self.total_tokens
+        s = f"{total/1e8:.2f}亿" if total > 1e8 else (f"{total/1e4:.1f}万" if total > 1e4 else str(total))
+        lines = [f"条目: {len(self.items)}", f"总token: {s}", f"分布:"]
+        for p, t in sorted(self.persona_distribution.items(), key=lambda x:-x[1]):
+            pct = t/total*100 if total else 0
+            lines.append(f"  {p}: {t/1e4:.1f}万 ({pct:.1f}%)")
+        jc = sum(1 for i in self.items if i.cognitive_jumps)
+        lines.append(f"含跃迁标注: {jc}/{len(self.items)}")
+        return '\n'.join(lines)
+    
+    def to_jsonl(self, fp: str, fmt: str = "text"):
+        with open(fp,'w',encoding='utf-8') as f:
+            for item in self.items:
+                f.write(json.dumps({"text":item.to_training_sample(fmt),"metadata":{"source_id":item.source_id,"hldp_path":item.hldp_path,"persona":item.persona,"quality_score":item.quality_score,"tokens_estimate":item.estimate_tokens()}},ensure_ascii=False)+'\n')