Datasetpublic

choucsan/Gaokao-Compass-11M

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… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/Gaokao-Compass-11M.

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Dataset Card

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.

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)

Directory Structure

text

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/
    └── ...

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)

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	totalcandidates, categories, batchlines	Total candidates, batch cutoff scores
university_stats	total, is985, is211, public, private	University counts by type
enrollment_stats	totalplan, universitycount, top_universities	Enrollment plan summary
top_majors	name, plancount, universitycount	Most popular majors
score_distribution	score, segmentcount, cumulativecount	Sampled score distribution
admission_bands	minscore, maxscore, median_score	Score ranges per batch

Example (Zhejiang 2024):

json

{
  "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
  }
}

Quick Start

Load from Hugging Face

python

from datasets import load_dataset

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

Read a CSV directly

python

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

python

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

python

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']}")

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

2025 Coverage

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

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

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。

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）

数据结构

text

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/
    └── ...

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

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	totalcandidates, categories, batchlines	高考人数、批次控制线
university_stats	total, is985, is211, public, private	院校统计
enrollment_stats	totalplan, universitycount, top_universities	招生计划统计
top_majors	name, plancount, universitycount	热门专业排名
score_distribution	score, segmentcount, cumulativecount	分数分布采样
admission_bands	minscore, maxscore, median_score	各批次录取分数段

示例（2024 浙江）：

json

{
  "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
  }
}

快速使用

从 Hugging Face 加载

python

from datasets import load_dataset

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

直接读取 CSV

python

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

python

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

python

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")

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

2025 年数据覆盖

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

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

许可证

MIT License

Citation / 引用

bibtex

@misc{zhishan_zou_2026,
  author       = { Zhishan Zou },
  title        = { Gaokao-Compass-11M (Revision 30b9233) },
  year         = 2026,
  url          = { https://huggingface.co/datasets/choucsan/Gaokao-Compass-11M },
  doi          = { 10.57967/hf/9882 },
  publisher    = { Hugging Face }
}

联系方式

如有问题、纠错或合作需求：

choucisan@gmail.com