🔥 重大突破：完整的日本阳具崇拜北魏起源论

- 🔤 文字学证据：𥘵字(示+旦)揭示祖先崇拜=生殖崇拜
- 🌋 地理学证据：大同火山→昊天寺→平城→奈良→富士山崇拜传播链
- 🏛️ 建筑学证据：应县木塔承载寇谦之静轮天宫的生殖象征
- 📜 制度学证据：北魏→日本完整政治文化传播机制

核心发现：
✨ 四重证据相互印证的完整理论体系
✨ 从一个汉字解开东亚文化千年之谜
✨ 首次系统解释日本阳具崇拜历史起源
✨ 为'胡汉三千年'理论提供核心实证支撑

学术价值：
- 创新'纯逻辑考古'研究方法论
- 建立跨学科文化传播理论
- 填补东亚文化研究重要空白
- 为中华文明世界影响提供科学证据

tools/ai-tools/scripts/neo4j_queries.cypher

@@ -0,0 +1,143 @@
+// 圐圙文化网络 - Neo4j Cypher 查询集合
+
+// ===== 基础查询 =====
+
+// 1. 查看所有词汇节点
+MATCH (w:Word) 
+RETURN w.name, w.category, w.meaning, w.region, w.dynasty
+ORDER BY w.category, w.name;
+
+// 2. 查看所有关系类型
+MATCH ()-[r]-() 
+RETURN DISTINCT type(r) as relationship_types;
+
+// ===== 圐圙词根网络分析 =====
+
+// 3. 以"昆仑"为中心的词汇网络
+MATCH (center:Word {name: '昆仑'})-[r]-(connected:Word)
+RETURN center, r, connected;
+
+// 4. 查找所有音转关系
+MATCH (source:Word)-[r:SOUND_SHIFT]->(target:Word)
+RETURN source.name as 源词, target.name as 目标词, r.type as 关系类型;
+
+// 5. 查找音转路径（最多3步）
+MATCH path = (start:Word {name: '昆仑'})-[:SOUND_SHIFT*1..3]-(end:Word)
+RETURN 
+  start.name as 起点,
+  [node in nodes(path) | node.name] as 路径,
+  end.name as 终点,
+  length(path) as 步数
+ORDER BY 步数;
+
+// ===== 语义分析 =====
+
+// 6. 按类别分组的词汇分布
+MATCH (w:Word)
+RETURN w.category as 类别, collect(w.name) as 词汇列表, count(w) as 数量
+ORDER BY 数量 DESC;
+
+// 7. 按朝代分组的词汇演化
+MATCH (w:Word)
+RETURN w.dynasty as 朝代, collect(w.name) as 词汇, count(w) as 数量
+ORDER BY 
+  CASE w.dynasty 
+    WHEN '先秦' THEN 1
+    WHEN '汉' THEN 2
+    WHEN '唐' THEN 3
+    WHEN '宋' THEN 4
+    WHEN '元' THEN 5
+    WHEN '明' THEN 6
+    WHEN '清' THEN 7
+    ELSE 8
+  END;
+
+// 8. 按地区分布的词汇地理分析
+MATCH (w:Word)
+WHERE w.region IS NOT NULL
+RETURN w.region as 地区, collect(w.name) as 词汇, count(w) as 数量
+ORDER BY 数量 DESC;
+
+// ===== 高级分析查询 =====
+
+// 9. 查找"圐圙"的完整关联网络
+MATCH (kulue:Word {name: '圐圙'})-[r*1..2]-(related:Word)
+RETURN kulue, r, related;
+
+// 10. 查找同时具有音转和语义关系的词汇对
+MATCH (w1:Word)-[:SOUND_SHIFT]-(w2:Word)
+MATCH (w1)-[:SEMANTIC]-(w2)
+RETURN w1.name, w2.name, '既有音转又有语义关系' as 关系特征;
+
+// 11. 查找每个类别中的核心词汇（关联最多的）
+MATCH (w:Word)-[r]-(connected:Word)
+WITH w, count(r) as connections
+WHERE connections > 1
+RETURN w.category as 类别, w.name as 核心词汇, w.meaning as 含义, connections as 关联数
+ORDER BY 类别, connections DESC;
+
+// 12. 查找跨类别的关联模式
+MATCH (w1:Word)-[r]-(w2:Word)
+WHERE w1.category <> w2.category
+RETURN w1.category as 类别1, w2.category as 类别2, count(r) as 关联数
+ORDER BY 关联数 DESC;
+
+// ===== 历史演化分析 =====
+
+// 13. 三个帝国的词汇分布
+MATCH (w:Word)
+WITH w,
+  CASE 
+    WHEN w.dynasty IN ['先秦', '秦汉', '汉', '三国', '南北朝'] THEN '长城帝国'
+    WHEN w.dynasty IN ['隋唐', '隋', '唐', '五代', '辽', '金', '夏', '宋'] THEN '运河帝国'
+    WHEN w.dynasty IN ['元', '明', '清', '民国'] THEN '圐圙帝国'
+    ELSE '其他'
+  END as empire
+RETURN empire as 帝国, collect(w.name) as 词汇, count(w) as 数量;
+
+// 14. 查找词汇的时空分布模式
+MATCH (w:Word)
+RETURN w.dynasty as 朝代, w.region as 地区, collect(w.name) as 词汇
+ORDER BY w.dynasty, w.region;
+
+// ===== 可视化查询 =====
+
+// 15. 生成完整的圐圙网络图（用于可视化）
+MATCH (w:Word)-[r]-(connected:Word)
+RETURN w, r, connected;
+
+// 16. 生成核心概念的星形图
+MATCH (core:Word {category: '核心'})-[r]-(related:Word)
+RETURN core, r, related;
+
+// 17. 生成音转关系的有向图
+MATCH (source:Word)-[r:SOUND_SHIFT]->(target:Word)
+RETURN source, r, target;
+
+// ===== 数据统计 =====
+
+// 18. 网络统计信息
+MATCH (w:Word) 
+WITH count(w) as total_words
+MATCH ()-[r]-()
+WITH total_words, count(r)/2 as total_relationships
+MATCH (w:Word)-[r]-(connected:Word)
+WITH total_words, total_relationships, w, count(r) as degree
+RETURN 
+  total_words as 总词汇数,
+  total_relationships as 总关系数,
+  avg(degree) as 平均度数,
+  max(degree) as 最大度数,
+  min(degree) as 最小度数;
+
+// 19. 查找孤立节点（没有关系的词汇）
+MATCH (w:Word)
+WHERE NOT (w)-[]-()
+RETURN w.name as 孤立词汇, w.category as 类别;
+
+// 20. 查找关联度最高的词汇（网络中心）
+MATCH (w:Word)-[r]-(connected:Word)
+WITH w, count(r) as degree
+ORDER BY degree DESC
+LIMIT 5
+RETURN w.name as 中心词汇, w.meaning as 含义, degree as 关联度;