目录

下面进入工程实现层（可直接部署的代码与系统设计）。目标：把你上面的架构真正跑起来，形成一个自动化内容生产 + 去AI痕迹 + 发布 + 反馈闭环系统。

一、整体工程架构（代码级）

project/
├── data_engine/
├── query_engine/
├── topic_engine/
├── content_engine/
├── deai_engine/
├── seo_engine/
├── publish_engine/
├── feedback_engine/
├── scheduler/
└── config/

二、Query Engine（查询生成器）

1️⃣ Query Fan-Out（Python实现）

import itertools

TEMPLATES = [
    "what is {}",
    "how to {}",
    "why {}",
    "{} vs {}",
    "best {} tools",
    "{} examples",
]

def generate_queries(seed_keyword):
    queries = []
    for tpl in TEMPLATES:
        if "{} vs {}" in tpl:
            queries.append(tpl.format(seed_keyword, "traditional SEO"))
        else:
            queries.append(tpl.format(seed_keyword))
    return list(set(queries))


if __name__ == "__main__":
    seed = "AI SEO"
    print(generate_queries(seed))

2️⃣ Embedding扩展（语义扩展）

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer('all-MiniLM-L6-v2')

def expand_queries(base_query, corpus):
    base_vec = model.encode([base_query])
    corpus_vecs = model.encode(corpus)

    sims = cosine_similarity(base_vec, corpus_vecs)[0]
    ranked = sorted(zip(corpus, sims), key=lambda x: x[1], reverse=True)

    return [q for q, _ in ranked[:20]]

三、Topic Mesh Engine

3️⃣ Topic Graph构建

import networkx as nx

G = nx.DiGraph()

def add_topic(parent, child):
    G.add_edge(parent, child)

add_topic("AI SEO", "AI Overview Ranking")
add_topic("AI SEO", "Passage Authority")
add_topic("AI Overview Ranking", "ranking factors")

print(G.edges())

👉 输出：语义网络结构（用于内链 + 生成路径）

四、Content Engine（内容生成）

4️⃣ 分阶段生成（关键）

Stage 1：结构生成

def generate_outline(query):
    return {
        "title": f"{query.title()} - Complete Guide",
        "sections": [
            "definition",
            "how it works",
            "examples",
            "common mistakes",
            "faq"
        ]
    }

Stage 2：段落生成（伪代码）

def generate_paragraph(section, query):
    prompt = f"""
    Write a high-entropy paragraph about {section} of {query}.
    Avoid generic phrasing. Include non-linear insights.
    """
    return call_llm(prompt)

Stage 3：FAQ生成

def generate_faq(query):
    return [
        {
            "q": f"What is {query}?",
            "a": call_llm(f"Define {query} concisely.")
        },
        {
            "q": f"How does {query} work?",
            "a": call_llm(f"Explain how {query} works.")
        }
    ]

五、De-AI Engine（核心算法实现）

5️⃣ 句法扰动（核心代码）

import random

def perturb_sentence(text):
    sentences = text.split(".")
    new_sentences = []

    for s in sentences:
        if len(s.split()) > 12:
            parts = s.split(",")
            random.shuffle(parts)
            new_sentences.append(". ".join(parts))
        else:
            new_sentences.append(s)

    return ". ".join(new_sentences)

6️⃣ 模板去除

TEMPLATE_PHRASES = [
    "In conclusion",
    "It is important to note",
    "In today's world"
]

def remove_templates(text):
    for phrase in TEMPLATE_PHRASES:
        text = text.replace(phrase, "")
    return text

7️⃣ 信息密度重排

def inject_entropy(paragraphs):
    result = []
    for i, p in enumerate(paragraphs):
        if i % 2 == 0:
            result.append(p.upper())  # 高密度模拟
        else:
            result.append(p.lower())  # 低密度模拟
    return result

六、SEO Engine（结构化）

8️⃣ 自动生成HTML结构

def build_html(content):
    html = f"<h1>{content['title']}</h1>"

    for sec in content['sections']:
        html += f"<h2>{sec}</h2>"
        html += f"<p>{sec}_content</p>"

    return html

9️⃣ Schema生成

import json

def generate_faq_schema(faqs):
    schema = {
        "@context": "https://schema.org",
        "@type": "FAQPage",
        "mainEntity": []
    }

    for f in faqs:
        schema["mainEntity"].append({
            "@type": "Question",
            "name": f["q"],
            "acceptedAnswer": {
                "@type": "Answer",
                "text": f["a"]
            }
        })

    return json.dumps(schema)

七、Publishing Engine（WordPress自动发布）

🔟 WordPress REST API发布

import requests

WP_URL = "https://your-site.com/wp-json/wp/v2/posts"
USERNAME = "admin"
PASSWORD = "application_password"

def publish_post(title, content):
    response = requests.post(
        WP_URL,
        auth=(USERNAME, PASSWORD),
        json={
            "title": title,
            "content": content,
            "status": "publish"
        }
    )
    return response.json()

八、内链自动化（核心SEO）

11️⃣ 自动内链插入

def insert_internal_links(content, links):
    for anchor, url in links.items():
        content = content.replace(anchor, f'<a href="{url}">{anchor}</a>')
    return content

九、调度系统（批量生产）

12️⃣ Scheduler（生产队列）

import time

def run_pipeline(queries):
    for q in queries:
        outline = generate_outline(q)
        content = generate_paragraph("definition", q)

        content = perturb_sentence(content)
        content = remove_templates(content)

        html = build_html({
            "title": outline["title"],
            "sections": outline["sections"]
        })

        publish_post(outline["title"], html)

        time.sleep(2)  # 控制速率

十、反馈系统（数据驱动优化）

13️⃣ GSC数据处理（简化）

def optimize_strategy(impressions, clicks):
    ctr = clicks / impressions if impressions else 0

    if ctr < 0.02:
        return "improve title"
    elif ctr > 0.1:
        return "expand content"

十一、部署建议（真实生产环境）

技术栈建议：

模块 技术
后端 Python + FastAPI
队列 Celery / Redis
数据库 PostgreSQL
向量 FAISS
部署 Docker
调度 Airflow

十二、关键优化（决定成败）

✔ 必须做：

  • 每段独立生成（Passage级）
  • Topic Mesh内链
  • 去AI痕迹处理
  • FAQ + Definition块

❌ 禁止：

  • 一次生成整篇文章直接发布
  • 同模板批量复制
  • 无内链结构

十三、一句话工程本质

这不是“写文章脚本”，而是一个“内容生成操作系统（Content OS）”。

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