humanize-chinese

name	humanize-chinese
description	Detect and humanize AI-generated Chinese text. 20+ detection categories, weighted 0-100 scoring with sentence-level analysis, 7 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo), sentence restructuring, context-aware replacement. Pure Python, no dependencies. v2.0.0
allowed-tools	["Read","Write","Edit","exec"]

Humanize Chinese AI Text v2.0

Comprehensive CLI for detecting and transforming Chinese AI-generated text. Makes robotic AI writing natural and human-like.

v2.0 highlights: weighted 0-100 scoring, sentence-level analysis, sentence restructuring (merge/split), context-aware replacement, rhythm variation, vocabulary diversification, 7 style transforms, external pattern config (patterns_cn.json).

Quick Start

# Detect AI patterns (20+ categories, 0-100 score)
python scripts/detect_cn.py text.txt
python scripts/detect_cn.py text.txt -v          # verbose + worst sentences
python scripts/detect_cn.py text.txt -s           # score only
python scripts/detect_cn.py text.txt -j           # JSON output

# Humanize text
python scripts/humanize_cn.py text.txt -o clean.txt
python scripts/humanize_cn.py text.txt --scene social
python scripts/humanize_cn.py text.txt --scene tech -a   # aggressive mode
python scripts/humanize_cn.py text.txt --seed 42         # reproducible

# Apply writing styles
python scripts/style_cn.py text.txt --style zhihu -o zhihu.txt
python scripts/style_cn.py text.txt --style xiaohongshu
python scripts/style_cn.py --list

# Compare before/after
python scripts/compare_cn.py text.txt --scene tech -a
python scripts/compare_cn.py text.txt -o clean.txt

---

Detection System

Scoring

Weighted 0-100 score with 4 severity levels:

Score	Level	Meaning
0-24	LOW	Likely human-written
25-49	MEDIUM	Some AI signals
50-74	HIGH	Probably AI-generated
75-100	VERY HIGH	Almost certainly AI

Detection Categories

🔴 Critical (weight: 8)

Category	Examples
Three-Part Structure	首先...其次...最后, 一方面...另一方面, 其一...其二...其三
Mechanical Connectors	值得注意的是, 综上所述, 不难发现, 归根结底, 由此可见
Empty Grand Words	赋能, 闭环, 数字化转型, 协同增效, 全方位, 多维度

🟠 High Signal (weight: 4)

Category	Examples
AI High-Frequency Words	助力, 彰显, 底层逻辑, 抓手, 触达, 沉淀, 复盘
Filler Phrases	值得一提的是, 众所周知, 毫无疑问
Balanced Arguments	虽然...但是...同时, 既有...也有...更有
Template Sentences	随着...的不断发展, 在当今...时代, 作为...的重要组成部分

🟡 Medium Signal (weight: 2)

Category	Examples
Hedging Language	在一定程度上, 某种程度上, 通常情况下 (>5 occurrences)
List Addiction	Excessive numbered/bulleted lists
Punctuation Overuse	Dense em dashes, semicolons
Excessive Rhetoric	对偶/排比句过多

⚪ Style Signal (weight: 1.5)

Category	Description
Uniform Paragraphs	Low CV in paragraph lengths
Low Burstiness	Monotonous sentence lengths
Emotional Flatness	Lack of emotional/personal expressions
Repetitive Starters	Same sentence starters >3 times
Low Entropy	Low character-level entropy (predictable text)

Sentence-Level Analysis

With -v (verbose) mode, the detector identifies the most AI-like sentences:

── 最可疑句子 ──
  1. [16分] 随着人工智能技术的不断发展，在当今数字化转型时代...
     原因: 数字化转型, 深度融合, 模板: 随着.*?的(不断)?发展

---

Humanization Engine

Transforms (applied in order)

1. Structure cleanup — Remove three-part structure (首先/其次/最后)
2. Phrase replacement — Context-aware replacement of AI phrases (regex patterns first, then plain text, longest-first matching)
3. Sentence merge — Merge overly short consecutive sentences
4. Sentence split — Split long sentences at natural breakpoints (但是/不过/同时)
5. Punctuation normalization — Reduce excessive semicolons, em dashes
6. Vocabulary diversification — Replace repeated words (进行/实现/提供 etc.) with synonyms
7. Paragraph rhythm — Vary uniform paragraph lengths (merge short, split long)
8. Casual injection — Add human expressions (scene-dependent)
9. Paragraph shortening — For social/chat scenes

Scenes

Scene	Casualness	Best For
general	0.3	Default, balanced
social	0.7	Social media, short posts
tech	0.3	Tech blogs, tutorials
formal	0.1	Formal articles, reports
chat	0.8	Conversations, messaging

Aggressive Mode (-a)

Adds +0.3 casualness, more colloquial expressions, stronger sentence restructuring. Typical score reduction: 60-80 points on heavily AI-generated text.

Reproducibility

Use --seed N for reproducible results (same input + seed = same output).

---

Writing Style Transforms

7 specialized Chinese writing styles:

Style	Name	Description
casual	口语化	Like chatting with friends — natural, relaxed
zhihu	知乎	Rational, in-depth, personal opinions
xiaohongshu	小红书	Enthusiastic, emoji-rich, product-focused
wechat	公众号	Storytelling, engaging, relatable
academic	学术	Rigorous, precise, no colloquialisms
literary	文艺	Poetic, imagery-rich, metaphorical
weibo	微博	Short, opinionated, shareable

Combine humanize + style

python scripts/humanize_cn.py text.txt --style xiaohongshu -o xhs.txt

This first humanizes (removes AI patterns) then applies the style transform.

---

External Configuration

All patterns, replacements, and scoring weights are in scripts/patterns_cn.json. Edit this file to:

• Add new AI vocabulary patterns
• Customize replacement alternatives
• Adjust scoring weights per severity
• Add regex patterns for template detection
• Set thresholds for hedging language detection

---

Scripts Reference

detect_cn.py

python scripts/detect_cn.py [file] [-j] [-s] [-v] [--sentences N]

Flag	Description
-j	JSON output
-s	Score only (e.g. "72/100 (high)")
-v	Verbose: show worst sentences
--sentences N	Number of worst sentences to show (default: 5)

humanize_cn.py

python scripts/humanize_cn.py [file] [-o output] [--scene S] [--style S] [-a] [--seed N]

Flag	Description
-o	Output file
--scene	general/social/tech/formal/chat
--style	casual/zhihu/xiaohongshu/wechat/academic/literary/weibo
-a	Aggressive mode
--seed	Random seed for reproducibility

style_cn.py

python scripts/style_cn.py [file] --style S [-o output] [--seed N] [--list]

compare_cn.py

python scripts/compare_cn.py [file] [-o output] [--scene S] [--style S] [-a]

Shows score diff, category changes, and metric comparison before/after humanization.

---

Workflow

# 1. Check AI score
python scripts/detect_cn.py document.txt -v

# 2. Humanize with comparison
python scripts/compare_cn.py document.txt --scene tech -a -o clean.txt

# 3. Verify improvement
python scripts/detect_cn.py clean.txt -s

# 4. Optional: apply specific style
python scripts/style_cn.py clean.txt --style zhihu -o final.txt

---

Batch Processing

# Scan all files
for f in *.txt; do
  echo "=== $f ==="
  python scripts/detect_cn.py "$f" -s
done

# Transform all markdown
for f in *.md; do
  python scripts/humanize_cn.py "$f" --scene tech -a -o "${f%.md}_clean.md"
done