ghostwyx/Gaokao-Compass-11M

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English

GaokaoCompass — China College Admission Dataset

GaokaoCompass is a structured dataset of China's national college entrance examination (Gaokao) admission records, covering all 31 provinces from 2017 to 2025. It includes enrollment plans, university admission cutoff scores, major-level admission scores, and score-ranking tables. The dataset is designed to help students, parents, and researchers make informed decisions with transparent, queryable admission data.

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Overview

Metric	Value
Provinces	31
Year span	2017–2025
Total CSV files	861
Total data rows	11,327,563
Full coverage (all 4 tables)	31 provinces × 2022–2025

Per-table statistics

Table	Files	Rows	Description
enrollment-plan	227	5,862,063	University enrollment plans by province
major-admission	218	4,497,048	Major-level admission scores
school-admission	227	763,558	University-level admission cutoff scores
score-range	189	204,894	Score-ranking tables (candidate distribution)

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Directory Structure

data/
├── 2017/
│   ├── anhui/
│   │   ├── enrollment-plan.csv        # Enrollment plan
│   │   ├── school-admission.csv       # University admission cutoff
│   │   ├── major-admission.csv        # Major-level admission scores
│   │   ├── score-range.csv            # Score-ranking table
│   │   └── meta.json                  # Summary statistics (6 dashboard cards)
│   ├── beijing/
│   │   └── ...
│   └── ... (31 provinces)
├── 2018/
│   └── ...
└── 2025/
    └── ...

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Table Schemas

1. score-range.csv — Score-Ranking Table

Shows how many candidates scored at each point level, enabling rank lookup.

Field	Type	Description
province	string	Province name
year	int	Year
category	string	Subject track (理科/文科/物理类/历史类/综合)
batch	string	Admission batch
control_score	int	Batch cutoff score
score	int	Score point
score_range	string	Score range label
segment_count	int	Number of candidates at this score
cumulative_count	int	Cumulative count (rank)
rank_range	string	Rank range label

2. enrollment-plan.csv — Enrollment Plan

Each row is one university-major enrollment slot in a province.

Field	Type	Description
province	string	Province
year	int	Year
batch	string	Batch (本科一批/本科二批/专科批 etc.)
category	string	Subject track
university_code	string	University code
university_name	string	University name
major_group	string	Major group
major_code	string	Major code
major_name	string	Major name
major_note	string	Major notes
subject_req	string	Subject requirements
plan_count	int	Planned enrollment count
duration	string	Program duration
tuition	float	Annual tuition (CNY)

3. school-admission.csv — University Admission Cutoff

Each row is one university's minimum admission score in a province.

Field	Type	Description
province	string	Province
year	int	Year
category	string	Subject track
batch	string	Batch
university_code	string	University code
university_name	string	University name
major_group	string	Major group
subject_req	string	Subject requirements
min_score	int	Minimum admission score
min_rank	int	Minimum admission rank
control_score	int	Provincial batch cutoff
score_diff	int	Score above batch cutoff
admit_count	int	Number admitted
school_province	string	Province where university is located
school_nature	string	Ownership (公办 public / 民办 private)
is_985	int	Project 985 university (1/0)
is_211	int	Project 211 university (1/0)

4. major-admission.csv — Major-Level Admission Scores

Each row is one university-major's admission detail in a province.

Field	Type	Description
province	string	Province
year	int	Year
category	string	Subject track
batch	string	Batch
university_code	string	University code
university_name	string	University name
major_group	string	Major group
major_code	string	Major code
major_name	string	Major name
major_note	string	Major notes
subject_req	string	Subject requirements
min_score	int	Minimum score
min_rank	int	Minimum rank
max_score	int	Maximum score
avg_score	float	Average score
admit_count	int	Number admitted
school_province	string	University location
school_nature	string	Ownership
is_985	int	Project 985 (1/0)
is_211	int	Project 211 (1/0)

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meta.json — Summary Statistics

Each province/year directory contains a meta.json with pre-computed statistics for 6 dashboard cards.

Card	Key fields	Description
exam_overview	total_candidates, categories, batch_lines	Total candidates, batch cutoff scores
university_stats	total, is_985, is_211, public, private	University counts by type
enrollment_stats	total_plan, university_count, top_universities	Enrollment plan summary
top_majors	name, plan_count, university_count	Most popular majors
score_distribution	score, segment_count, cumulative_count	Sampled score distribution
admission_bands	min_score, max_score, median_score	Score ranges per batch

Example (Zhejiang 2024):

{
  "exam_overview": {
    "categories": [
      {
        "name": "综合",
        "total_candidates": 281057,
        "max_score": 699,
        "batch_lines": [
          {"batch": "平行录取一段", "score": 492},
          {"batch": "平行录取二段", "score": 269}
        ]
      }
    ],
    "total_candidates": 281057
  },
  "university_stats": {
    "total": 1608,
    "is_985": 37,
    "is_211": 108,
    "public": 900,
    "private": 400
  }
}

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Quick Start

Load from Hugging Face

from datasets import load_dataset

dataset = load_dataset("choucsan/Gaokao-Compass-11M")

Read a CSV directly

import pandas as pd

# Load 2024 Zhejiang enrollment plan
df = pd.read_csv("data/2024/zhejiang/enrollment-plan.csv")

# Search for Computer Science majors
cs = df[df["major_name"].str.contains("计算机科学与技术", na=False)]
print(cs[["university_name", "major_name", "plan_count", "tuition"]])

Look up a score ranking

import pandas as pd

# Load 2024 Henan score-ranking table
df = pd.read_csv("data/2024/henan/score-range.csv")

# Find rank for a score of 600
row = df[df["score"] == 600]
print(f"Rank at 600: {row['cumulative_count'].values[0]}")

Use meta.json

import json

with open("data/2024/zhejiang/meta.json", encoding="utf-8") as f:
    meta = json.load(f)

print(f"Total candidates: {meta['exam_overview']['total_candidates']}")
print(f"Universities: {meta['university_stats']['total']}")
print(f"Project 985 universities: {meta['university_stats']['is_985']}")

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Data Pipeline

Raw data is sourced from provincial education examination authorities and the National Education Examination Authority (阳光高考). The ETL pipeline:

1. Discovery — Scan raw Excel files per province, classify by type (enrollment plan / school admission / major admission / score range)
2. Column normalization — Map 30+ Chinese column name variants to unified English fields
3. Cleaning — Normalize subject tracks, safe numeric casting, strip decimals from code fields
4. Deduplication — MD5-based file dedup; row-level dedup by (university, batch, category, major_code)
5. Year extraction — Parse year from filenames and directory paths
6. Validation — Null-rate checks, error summary, completeness report

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2025 Coverage

Provinces	enrollment-plan	school-admission	major-admission	score-range
28 provinces	✅	✅	✅	✅
Qinghai (青海)	✅	❌	❌	❌
Shanxi (山西)	✅	✅	❌	✅
Tibet (西藏)	✅	✅	✅	❌

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Use Cases

• College application — Query eligible universities and majors by score and rank
• Admission trend analysis — Visualize cutoff score and enrollment changes over years
• University comparison — Compare admission difficulty across 985/211/public/private institutions
• Major popularity — Rank majors by enrollment quota and number of offering universities
• Education research — Academic studies and policy analysis on Gaokao data

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中文

高考录取数据平台 · GaokaoCompass

GaokaoCompass 是一个面向中国高考（普通高等学校招生全国统一考试）的结构化录取数据集，覆盖 31 个省份、2017–2025 年的招生计划、院校投档线、专业录取分数和一分一段表数据。旨在为考生、家长和教育研究者提供透明、可查询的高考录取信息参考。

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数据概览

指标	数值
省份	31
年份跨度	2017–2025
数据文件总数	861 个 CSV
数据总行数	11,327,563
四表齐全省份/年份	2022–2025 年 31 省全覆盖

各表统计

数据表	文件数	数据行数	说明
enrollment-plan	227	5,862,063	各省高校招生计划
major-admission	218	4,497,048	各高校专业录取分数
school-admission	227	763,558	各高校院校投档线
score-range	189	204,894	一分一段表（考生排名）

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数据结构

data/
├── 2017/
│   ├── anhui/
│   │   ├── enrollment-plan.csv        # 招生计划
│   │   ├── school-admission.csv       # 院校投档线
│   │   ├── major-admission.csv        # 专业录取分数
│   │   ├── score-range.csv            # 一分一段表
│   │   └── meta.json                  # 统计摘要（6类卡片）
│   ├── beijing/
│   │   └── ...
│   └── ...（31省）
├── 2018/
│   └── ...
└── 2025/
    └── ...

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四类数据表说明

1. score-range.csv · 一分一段表

考生分数排名数据，反映每个分数段的考生人数分布。

字段	类型	说明
province	string	省份
year	int	年份
category	string	科类（理科/文科/物理类/历史类/综合）
batch	string	批次
control_score	int	批次控制线
score	int	分数
score_range	string	分数区间
segment_count	int	本段人数
cumulative_count	int	累计人数（排名）
rank_range	string	排名区间

2. enrollment-plan.csv · 招生计划

各高校在各省的招生专业和计划人数。

字段	类型	说明
province	string	省份
year	int	年份
batch	string	批次（本科一批/本科二批/专科批等）
category	string	科类
university_code	string	院校代码
university_name	string	院校名称
major_group	string	专业组
major_code	string	专业代码
major_name	string	专业名称
major_note	string	专业备注
subject_req	string	选科要求
plan_count	int	计划人数
duration	string	学制
tuition	float	学费（元/年）

3. school-admission.csv · 院校投档线

各高校在各省的最低录取分数和位次。

字段	类型	说明
province	string	省份
year	int	年份
category	string	科类
batch	string	批次
university_code	string	院校代码
university_name	string	院校名称
major_group	string	专业组
subject_req	string	选科要求
min_score	int	最低分
min_rank	int	最低位次
control_score	int	省控线
score_diff	int	批次线差
admit_count	int	录取人数
school_province	string	学校所在省份
school_nature	string	办学性质（公办/民办）
is_985	int	是否 985 高校（1/0）
is_211	int	是否 211 高校（1/0）

4. major-admission.csv · 专业录取分数

各高校各专业在各省的录取分数详情。

字段	类型	说明
province	string	省份
year	int	年份
category	string	科类
batch	string	批次
university_code	string	院校代码
university_name	string	院校名称
major_group	string	专业组
major_code	string	专业代码
major_name	string	专业名称
major_note	string	专业备注
subject_req	string	选科要求
min_score	int	最低分
min_rank	int	最低位次
max_score	int	最高分
avg_score	float	平均分
admit_count	int	录取人数
school_province	string	学校所在省份
school_nature	string	办学性质
is_985	int	是否 985
is_211	int	是否 211

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meta.json · 统计摘要

每个省份/年份目录下包含一个 meta.json 文件，提供 6 类卡片的统计信息，适合直接用于前端可视化展示。

卡片	字段	说明
exam_overview	total_candidates, categories, batch_lines	高考人数、批次控制线
university_stats	total, is_985, is_211, public, private	院校统计
enrollment_stats	total_plan, university_count, top_universities	招生计划统计
top_majors	name, plan_count, university_count	热门专业排名
score_distribution	score, segment_count, cumulative_count	分数分布采样
admission_bands	min_score, max_score, median_score	各批次录取分数段

示例（2024 浙江）：

{
  "exam_overview": {
    "categories": [
      {
        "name": "综合",
        "total_candidates": 281057,
        "max_score": 699,
        "batch_lines": [
          {"batch": "平行录取一段", "score": 492},
          {"batch": "平行录取二段", "score": 269}
        ]
      }
    ],
    "total_candidates": 281057
  },
  "university_stats": {
    "total": 1608,
    "is_985": 37,
    "is_211": 108,
    "public": 900,
    "private": 400
  }
}

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快速使用

从 Hugging Face 加载

from datasets import load_dataset

dataset = load_dataset("choucsan/Gaokao-Compass-11M")

直接读取 CSV

import pandas as pd

# 读取 2024 年浙江的招生计划
df = pd.read_csv("data/2024/zhejiang/enrollment-plan.csv")

# 查询计算机科学与技术专业的招生计划
cs = df[df["major_name"].str.contains("计算机科学与技术", na=False)]
print(cs[["university_name", "major_name", "plan_count", "tuition"]])

查询一分一段表

import pandas as pd

# 读取 2024 年河南理科一分一段表
df = pd.read_csv("data/2024/henan/score-range.csv")

# 查看 600 分对应的排名
row = df[df["score"] == 600]
print(f"600分排名: {row['cumulative_count'].values[0]}")

使用 meta.json

import json

with open("data/2024/zhejiang/meta.json", encoding="utf-8") as f:
    meta = json.load(f)

print(f"浙江2024高考人数: {meta['exam_overview']['total_candidates']}")
print(f"招生院校数: {meta['university_stats']['total']}")
print(f"985高校: {meta['university_stats']['is_985']} 所")

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数据来源

数据来自各省教育考试院、阳光高考平台等公开渠道，经清洗、标准化和去重后整理为统一格式。

处理流程：

1. 文件发现：扫描各省原始 Excel 文件，按类型自动分类
2. 列名标准化：30+ 种原始列名映射为统一英文字段
3. 数据清洗：科类标准化、数值安全转换、代码字段去小数点
4. 去重：按文件大小 + MD5 去除重复文件，专业条目按 (院校, 批次, 科类, 专业代码) 去重
5. 年份提取：从文件名和目录路径自动提取年份
6. 质量验证：空值率检查、错误汇总、数据完整性报告

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2025 年数据覆盖

省份	enrollment-plan	school-admission	major-admission	score-range
28 省	✅	✅	✅	✅
青海	✅	❌	❌	❌
山西	✅	✅	❌	✅
西藏	✅	✅	✅	❌

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应用场景

• 考生志愿填报：根据分数和排名查询可报考的院校和专业
• 录取趋势分析：历年分数线、招生人数变化趋势可视化
• 院校对比：985/211/公办/民办院校在各省的录取难度对比
• 专业热度分析：各专业招生计划人数和开设院校数量统计
• 教育研究：高考数据的学术研究和政策分析

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许可证

MIT License

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联系方式

如有问题、纠错或合作需求：

choucisan@gmail.com