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搜索一页 Google Scholar 搜索结果
方法:
POST返回: Google Scholar 搜索结果列表（抽取后的 JSON 数据）
说明：文档中的示例均以 国内入口 为例，如需使用国外入口，请将接口地址替换即可。
参数说明
| 参数名 | 类型 | 是否必须 | 默认值 | 说明 |
|---|---|---|---|---|
q | String | 是 | 无 | 搜索关键词。接口优先使用 keyword，若无则使用 q。 |
gl | String | 否 | US | 地理位置代码（如 US, CN 等）。 |
hl | String | 否 | en | 搜索语言，如 en, zh-cn。 |
page | Int | 否 | 1 | 页码。系统限制范围为 1-5。 |
year_to | Int | 否 | 无 | 截止年份。 |
sort_by_date | Bool | 否 | false | 是否按日期排序。支持 true, 1, yes, on 等真值。 |
include_patents | Bool | 否 | false | 是否包含专利。支持 true, 1, yes, on 等真值。 |
include_citations | Bool | 否 | true | 是否包含引用。支持 true, 1, yes, on 等真值。 |
日期筛选注意事项
不要在同一个请求里同时传 year_from 、 year_to 和 sort_by_date 。
这三个参数同时传时，Google Scholar 可能返回空结果。这是 Google Scholar 自身的行为，不是接口解析问题。
如果需要按日期排序，只传 sort_by_date=true ，不要同时传完整年份范围。
如果需要按年份范围筛选，只传 year_from 和/或 year_to ，不要同时传 sort_by_date 。
测试用例：Url参数类型
bash
curl --location 'https://api.serp.hk/serp/google/scholar/advanced' \
--header 'Authorization: Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx' \
--header 'Content-Type: application/json' \
--data '{
"q": "Deep Learning",
"gl": "US",
"hl": "en"
}' --compressed返回结果
json
{
"code": 0,
"msg": "成功",
"result": {
"general": {
"engine": "google",
"page": 1,
"query": "Deep Learning",
"type": "scholar"
},
"organic": [
{
"id": "0qfs6zbVakoJ",
"rank": 1,
"title": "Deep learning",
"link": "https://www.nature.com/articles/nature14539",
"display_link": "Y LeCun, Y Bengio, G Hinton - nature, 2015 - nature.com",
"source": "nature.com",
"publication_info": "Y LeCun, Y Bengio, G Hinton - nature, 2015 - nature.com",
"authors": "Y LeCun, Y Bengio, G Hinton - nature, 2015",
"year": 2015,
"snippet": "… Deep learning allows computational models that are composed of multiple processing layers to learn … Deep learning discovers intricate structure in large data sets by using the …",
"pdf_link": "https://hal.science/hal-04206682/document",
"cited_by": 111615,
"versions": 85
},
{
"id": "MHq4MMenr-gJ",
"rank": 2,
"title": "Deep learning",
"link": "https://synapse.koreamed.org/pdf/10.4258/hir.2016.22.4.351",
"display_link": "I Goodfellow, Y Bengio, A Courville, Y Bengio - 2016 - synapse.koreamed.org",
"source": "synapse.koreamed.org",
"publication_info": "I Goodfellow, Y Bengio, A Courville, Y Bengio - 2016 - synapse.koreamed.org",
"authors": "I Goodfellow, Y Bengio, A Courville, Y Bengio",
"year": 2016,
"snippet": "Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their careers in deep learning and artificial intelligence research. The other target audience …",
"pdf_link": "https://synapse.koreamed.org/pdf/10.4258/hir.2016.22.4.351",
"cited_by": 94151,
"versions": 30
},
{
"id": "6ZPEi3CJiiUJ",
"rank": 3,
"title": "Deep learning",
"link": "https://www.academia.edu/download/62266271/Deep_Learning20200303-80130-1s42zvt.pdf",
"display_link": "Y Bengio, I Goodfellow, A Courville - 2017 - academia.edu",
"source": "academia.edu",
"publication_info": "Y Bengio, I Goodfellow, A Courville - 2017 - academia.edu",
"authors": "Y Bengio, I Goodfellow, A Courville",
"year": 2017,
"snippet": "… The idea of learning the right representation for the data provides one perspective on deep learning. Another perspective on deep learning is that it allows the computer to learn a multi-…",
"pdf_link": "https://www.academia.edu/download/62266271/Deep_Learning20200303-80130-1s42zvt.pdf",
"cited_by": 2879,
"versions": 4
},
{
"id": "VK7xPyl_ZR0J",
"rank": 4,
"title": "Deep learning",
"link": "https://books.google.com/books?hl=en&lr=&id=b06qDwAAQBAJ&oi=fnd&pg=PR7&dq=Deep+Learning&ots=_pGZUKgVXQ&sig=XZZC_QWdrHjv0LiggA1gJEwjoCQ",
"display_link": "JD Kelleher - 2019 - books.google.com",
"source": "books.google.com",
"publication_info": "JD Kelleher - 2019 - books.google.com",
"authors": "JD Kelleher",
"year": 2019,
"snippet": "… a deep learning system… deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning …",
"cited_by": 869,
"versions": 5
},
{
"id": "iPZeWThU1H8J",
"rank": 5,
"title": "Deep learning",
"link": "https://www.nature.com/articles/nmeth.3707",
"display_link": "N Rusk - Nature Methods, 2016 - nature.com",
"source": "nature.com",
"publication_info": "N Rusk - Nature Methods, 2016 - nature.com",
"authors": "N Rusk - Nature Methods, 2016",
"year": 2016,
"snippet": "… Deep learning does not depend on prior data processing and automatically extracts features. To use a simple example, a deep neural network tasked with interpreting shapes …",
"cited_by": 428,
"versions": 5
},
{
"id": "IL7Ql8vemMoJ",
"rank": 6,
"title": "Deep learning techniques: an overview",
"link": "https://link.springer.com/chapter/10.1007/978-981-15-3383-9_54",
"display_link": "A Mathew, P Amudha, S Sivakumari - … on advanced machine learning …, 2020 - Springer",
"source": "link.springer.com",
"publication_info": "A Mathew, P Amudha, S Sivakumari - … on advanced machine learning …, 2020 - Springer",
"authors": "A Mathew, P Amudha, S Sivakumari - … on advanced machine learning …, 2020",
"year": 2020,
"snippet": "… of deep learning, various approaches to deep learning, architectures of deep learning, … the performance of traditional machine learning algorithms and deep learning algorithms [6]…",
"pdf_link": "https://www.researchgate.net/profile/Munish-Sabharwal/publication/341652471_An_Assessment_Study_of_Gait_Biometric_Recognition_Using_Machine_Learning/links/5f16d04ba6fdcc9626a438ec/An-Assessment-Study-of-Gait-Biometric-Recognition-Using-Machine-Learning.pdf#page=584",
"cited_by": 710,
"versions": 4
},
{
"id": "P3vCNmtVdp4J",
"rank": 7,
"title": "Deep learning",
"link": "https://link.springer.com/chapter/10.1007/978-981-95-1184-6_16",
"display_link": "B Chen, S Chen - Machine Vision Technology, 2026 - Springer",
"source": "link.springer.com",
"publication_info": "B Chen, S Chen - Machine Vision Technology, 2026 - Springer",
"authors": "B Chen, S Chen - Machine Vision Technology, 2026",
"year": 2026,
"snippet": "… deep learning, contrasting shallow models such as support vector machines and boosting with deep architectures that learn … The “ deep ” in deep learning represents this layered system. …",
"pdf_link": "https://pdfs.semanticscholar.org/0729/79b5af1ab7fc4c16a780abebacb5d0e43d12.pdf",
"cited_by": 1210,
"versions": 6
},
{
"id": "s9Kbb94saVEJ",
"rank": 8,
"title": "A review of machine learning and deep learning applications",
"link": "https://ieeexplore.ieee.org/abstract/document/8697857/",
"display_link": "PP Shinde, S Shah - 2018 Fourth international conference on …, 2018 - ieeexplore.ieee.org",
"source": "ieeexplore.ieee.org",
"publication_info": "PP Shinde, S Shah - 2018 Fourth international conference on …, 2018 - ieeexplore.ieee.org",
"authors": "PP Shinde, S Shah - 2018 Fourth international conference on …, 2018",
"year": 2018,
"snippet": "… So far few applications of deep learning … deep learning. A review of these past and future application domains, sub-domains, and applications of machine learning and deep learning …",
"cited_by": 1524,
"versions": 3
},
{
"id": "AuotyInCgE4J",
"rank": 9,
"title": "Deep learning",
"link": "http://www.scholarpedia.org/article/Deep_Learning",
"display_link": "J Schmidhuber - Scholarpedia, 2015 - scholarpedia.org",
"source": "scholarpedia.org",
"publication_info": "J Schmidhuber - Scholarpedia, 2015 - scholarpedia.org",
"authors": "J Schmidhuber - Scholarpedia, 2015",
"year": 2015,
"snippet": "…, may learn to solve problems of potentially unlimited depth, for example, by learning to store in … Some NNs can quickly learn to solve certain deep but simple problems through random …",
"html_url": "http://www.scholarpedia.org/article/Deep_Learning",
"cited_by": 256,
"versions": 4
},
{
"id": "japPSnLK4jwJ",
"rank": 10,
"title": "Deep learning",
"link": "https://www.worldscientific.com/doi/abs/10.1142/S1793351X16500045",
"display_link": "X Hao, G Zhang, S Ma - International Journal of Semantic …, 2016 - World Scientific",
"source": "worldscientific.com",
"publication_info": "X Hao, G Zhang, S Ma - International Journal of Semantic …, 2016 - World Scientific",
"authors": "X Hao, G Zhang, S Ma - International Journal of Semantic …, 2016",
"year": 2016,
"snippet": "Deep learning is a branch of machine learning that tries to model high-level abstractions of data … This paper provides an overview of deep learning in neural networks including popular …",
"pdf_link": "https://www.academia.edu/download/62046595/2019_Book_EEGSignalProcessingAndFeatureE20200209-88259-6zld91.pdf#page=326",
"cited_by": 436,
"versions": 8
}
]
}
}结果字段说明
| 字段 | 说明 |
|---|---|
| general | 通用信息（如搜索关键字、搜索引擎等） |
| organic | 相关自然搜索结果列表，每项包含网页标题、摘要、URL 等信息 |
错误码说明
| 错误码 | 说明 |
|---|---|
| 3101 | 参数错误 |
| 3102 | 访问失败，请重试 |
| 3103 | 未授权 |
| 3104 | 超过访问速率限制 |
| 3199 | 运行时错误 |