QInzhengk/Math-Model-and-Machine-Learning: A Chinese-Language Competition Archive, Not a Library

What problem this repository actually solves

Competition preparation in Chinese universities tends to scatter material across QQ groups, cloud drives, paid course platforms and personal blogs. Past problems and award-winning papers are the main study resource, and they are usually distributed as loose PDFs with inconsistent naming. This repository gathers that material into one Git history. The README lists sections for 全国大学生数学建模竞赛, 研究生数学建模竞赛, 美国大学生数学建模竞赛, MathorCup, 华中杯, 华数杯, APMCM, 深圳杯, 东北三省数学建模联赛, 中青杯, 五一数学建模竞赛, 全国大学生电工数学建模竞赛, 华东杯, 数学中国(认证杯) and 数维杯, alongside folders for 数学建模书籍, 数学建模模板, 数学建模算法 and 数学建模课件. There is also a 数学 folder with 大学生数学竞赛 and 阿里巴巴数学竞赛 problems and answers, and a 计算机学习 folder covering Java, Office and 小程序.

The intended reader is stated in the README: beginners in mathematical modeling and in machine learning, deep learning and large models. The author lists personal results (高考数学144, a first prize in the preliminary round of the 全国大学生数学竞赛, and a first prize in the 华为杯中国研究生数学建模竞赛) as context for why the notes are worth reading. That is a self-description, not an independent quality signal. The material is in Chinese, and the README is in Chinese, so the repository assumes a Chinese-reading audience throughout.

Folder layout and what the file tree tells you

The README prints the tree explicitly. Top level holds README.md plus 人工智能, 数学, 数学建模, 计算机学习 and picture. Inside 数学建模 the branches are 各个比赛的赛题和优秀论文, 数学建模书籍, 数学建模模板, 数学建模算法 and 数学建模课件. The competition branch then fans out per contest, and under each contest there are subfolders such as 全国大学生数学建模竞赛（92-21）, 国赛官方的评阅要点, 大学生国赛赛题讲解 and 本科数模优秀论文.

Two structural facts matter more than the list itself. First, the repository is organised by competition and by document type, not by topic or by technique. If you want every model that uses graph theory, you will be walking the whole tree; there is no index file beyond the README. Second, the README states that all files larger than 25 MB were not uploaded and are obtainable through the WeChat public account 数学建模与人工智能. That means the clone you get is a partial archive, and the missing pieces are exactly the kind of thing that tends to be large: scanned books, video, and full slide decks. The picture folder holds QR codes for the public account, a QQ group (970716977), a QQ channel and a Zhihu circle, which is how the project routes around GitHub's file size limits.

Getting the material onto your machine

There is nothing to install. The repository is a content archive, so the workflow is clone and read. The README's contribution section gives the standard fork-and-branch commands, which are the only commands it documents:

git checkout -b your_branch git commit -m 'Add some AmazingFeature' git push origin your_branch

To obtain the material itself, clone the default branch:

git clone https://github.com/QInzhengk/Math-Model-and-Machine-Learning.git

Because the primary language is Jupyter Notebook, expect .ipynb files in the 人工智能 and algorithm areas; those need a Jupyter environment to execute, though they render on GitHub without one. The competition papers are documents, so a PDF reader is the real dependency. There is no requirements.txt, no environment.yml, no Makefile and no package manifest in the README, and no releases were retrieved, so there is no versioned artifact to pin. If you are cloning for a specific contest, the practical move is a sparse checkout or a shallow clone rather than pulling the full history, since the repository is document-heavy and its size grows with each push.

The 25 MB cutoff is a real limitation, not a footnote

The README says plainly: 目前所有大于25M的文件都未进行上传. Anything over 25 MB lives behind the WeChat public account. For a repository whose value is largely scanned books and slide decks, this is the central constraint. You cannot audit the archive from the clone alone, because you cannot tell whether a given book chapter or lecture deck exists in the repository or only in the public account. The README's 待办事项 list is also unfinished, with 数学建模竞赛题目和优秀论文 and 人工智能 both unchecked, which suggests the maintainer considers the collection incomplete.

A second limitation is the licence. The metadata reports the licence as unknown, and the README does not state one. Competition papers, published books and course slides are third-party works with their own rights. Redistributing them in a public Git repository is a different act from reading them, and the repository gives no guidance on permitted use. Without a stated licence you also have no explicit grant to reuse the notebooks or templates in your own work. That is a factual gap, not a legal verdict, and anyone planning to republish material from here should resolve it before doing so.

How it compares with a maintained course or a paper index

The closest alternatives are not other GitHub repositories but different genres. A university course site or a platform like a MOOC gives you a sequenced curriculum with exercises and grading; this repository gives you raw competition material and personal notes with no ordering beyond the folder tree. A curated paper index such as an arXiv listing or a conference proceedings site gives you searchable metadata and stable citations; here you get filenames in Chinese inside nested directories, with no DOI or abstract layer. The difference in approach is that this project optimises for completeness of a specific national competition culture, while those resources optimise for discoverability and provenance.

For the machine learning portion specifically, a textbook with runnable code (for example a published book whose notebooks are versioned alongside the text) will teach you more per hour than a folder of notes, because the code and the explanation are kept in sync. This repository's notebooks are study notes, and the README does not claim they are tested or executable end to end. The honest comparison is: use this for the competition papers and the templates, and use a proper course for the concepts. The repository's own framing supports that reading, since it presents itself as a 分享 (a sharing) of notes and 资料 rather than a teaching system.

Maintenance, updates and the cost of following it

The last push recorded is 2026-09-10, and the README describes the project as 持续更新中. Updates arrive as ordinary commits to main, with no releases retrieved, so there is no changelog to read and no tagged snapshot to depend on. Practically, keeping current means pulling main and accepting whatever changed, including renamed Chinese folder paths, which will break any script or note that references a path by name. If you cite a specific paper or template, record the commit hash rather than the folder path.

The contribution path is the standard fork, branch, commit, pull request flow that the README spells out, and the README invites PRs to 共同将这个项目壮大. The maintenance burden on a consumer is low in one sense (nothing to upgrade) and high in another (no versioning, no licence clarity, no index). There is also a single named contributor, QZK, in the contributor table, so the pace of updates depends on one person. For a document archive that is tolerable; for anything you plan to build on, it is a dependency risk worth naming.

Who should clone this, and what to check first

Clone it if you are entering a Chinese mathematical modeling competition and want the past problems, the 评阅要点 and the 优秀论文 in one place, or if you are a Chinese-speaking beginner who wants notes on machine learning and deep learning without paying for a course. The 数学建模模板 and 数学建模算法 folders are the parts most likely to save you time during a competition weekend, and the 数学 folder covers the 大学生数学竞赛 and 阿里巴巴数学竞赛 material separately.

Skip it if you need an installable library, an English-language curriculum, or material with an explicit reuse licence. Skip it too if you need every file present locally, because the 25 MB rule guarantees gaps. Before you commit to it, do three things: clone the repository, open 数学建模/数学建模算法 and 人工智能 and confirm the notebooks you want are there and render; check whether the large files you need are only in the WeChat public account; and note that no licence is stated, so decide what you are willing to reuse before you copy anything into your own project.

Editorial conclusion

Adopt this repository if you are preparing for a Chinese mathematical modeling competition (CUMCM, MCM/ICM, MathorCup, APMCM and the others listed in the README) or starting self-study in machine learning and deep learning with Chinese-language material, and you want past papers, templates and algorithm notes in one clone. Do not adopt it if you need an installable library, English-language teaching, or a clearly licensed dataset, because the repository states no licence and the README itself notes that files above 25 MB are not uploaded. Before relying on it, run git clone, then open 数学建模/数学建模算法 and 人工智能 to confirm the notebooks you need are actually present and readable, and check whether the missing large files matter for your task.

Official sources

1. Issues
2. Project website
3. QInzhengk/Math-Model-and-Machine-Learning on GitHub
4. README