Silkline DataSilkline Data / Zhihu API / Search by keyword (answers, articles, pins, users, topics, columns, videos, ebooks, papers)
Real run · 2026-11-09 · 10 recordsZhihu Search by keyword API
Search by keyword (answers, articles, pins, users, topics, columns, videos, ebooks, papers) from Zhihu, returned as JSON rows you can save to CSV.
Resultready
JSON
10records in this run
12fields per record
12srun time, start to finish
$0.004 per item returnedper record, Bronze tier
Request
The input that produced this run
Mode search. Parameters as sent, with what each one does.
| Parameter | Value | What it does |
|---|---|---|
mode | "search" | What to scrape. Each mode reads only the fields named in its title. |
keyword | "机器学习" | Search keyword, Chinese or English (falls back to "python" when empty). |
searchType | "papers" | Answers and articles only, everything including pins, or one of the other searches: users, topics, columns, videos, ebooks or scholarly papers. Sort and time range apply only to the first three. |
maxResults | 10 | Maximum number of items to return in total for this run. You are charged per item returned. |
Response fields
12 fields in every paper
From the first record of the run above. Identity fields of ordinary users are masked on this site.
| Field | Type | Example |
|---|---|---|
type | str | paper |
paperId | str | 1984436974791190300 |
url | str | https://www.zhihu.com/kvip/sku/paper/1984436974791190300 |
title | str | 自动机器学习:框架、方法与挑战 |
excerpt | str | 近年来,机器学习得到了广泛的关注和快速的发展。然而,以人力构建高效率的机器学习模型是一个重复、复杂和耗时的过程,随... |
authors | str | <masked> |
keywords | list | ["自动机器学习", "机器学习", "模型优化", "自动机器学习平台"] |
journal | str | 微型电脑应用 |
year | int | 2025 |
volume | str | 41 |
issue | str | 10 |
indexedBy | list | [] |
Sample output
A real run, 12 fields per record
Run on 2026-11-09 with input {"mode": "search", "keyword": "机器学习", "searchType": "papers", "maxResults": 10}. Personal fields of ordinary users are masked here.
| title | excerpt | journal | year | volume | issue |
|---|---|---|---|---|---|
| 自动机器学习:框架、方法与挑战 | 近年来,机器学习得到了广泛的关注和快速的发展。然而,以人力构建高效率的机器学习模型是一个重复、复杂和耗时的过程,随... | 微型电脑应用 | 2025 | 41 | 10 |
| 机器学习在肺癌预后预测模型中的应用进展 | 机器学习算法在肺癌预后预测模型中的应用涵盖高斯朴素贝叶斯、支持向量机、随机森林、随机生存森林、梯度提升树、极限梯度... | 癌症进展 | 2025 | 23 | 19 |
| 机器学习在肺炎死亡预测模型中的研究进展 | 肺炎(pneumonia)是指各种致病因素导致的肺实质及肺间质炎症,是全球范围内常见的呼吸系统疾病之一,而重症肺炎... | 生物医学 | 2025 | 15 | 3 |
| 机器学习算法构建预测模型的特征优化方法 | 利用多种机器学习算法构建预测模型并筛选最优预测模型已成为预测模型研究领域的热点之一,本文针对特征选择后模型复杂度仍... | 中国医学前沿杂志(电子版) | 2025 | 17 | 11 |
| 机器学习赋能电子结构计算:进展、挑战与展望 | 密度泛函理论在当代电子结构计算中占据主流地位,然而其计算复杂度随体系规模呈立方增长,制约了在复杂体系或高精度计算中... | 物理学报 | 2026 | 75 | 1 |
| 机器学习在高熵合金研究中的应用进展 | 机器学习凭借在高维特征处理与非线性建模方面的优势,被广泛应用于高熵合金的成分筛选、相结构预测、力学与功能性能调控以... | 特种铸造及有色合金 | 2026 | 46 | 6 |
All 12 fields in one record
typepaperpaperId1984436974791190300urlhttps://www.zhihu.com/kvip/sku/paper/1984436974791190300title自动机器学习:框架、方法与挑战excerpt近年来,机器学习得到了广泛的关注和快速的发展。然而,以人力构建高效率的机器学习模型是一个重复、复杂和耗时的过程,随...authorskeywordsjournal微型电脑应用year2025volume41issue10indexedBy
Limits
Good to know before you run it
- Search. Ads, suggestions and other card types are left out. Pins appear only with result type
all. Answer and article full text is included in the search results. - Stability. The Actor retries transient errors on its own and stops cleanly instead of failing half-way. In
searchmode an empty page is retried once; other lists and lookups are requested once, and an empty answer is treated as no data. detailmode on an answer has novoteupCount,commentCountorfavoriteCount; usesearch,questionoruserContentfor those counts.
Developers
Run this request yourself
The same input as above, three ways.
Request
Response · 200application/json