📖 技能介绍

Google Scholar Search

Search academic papers using the free Semantic Scholar API. No API key required.

Quick Start

Basic search:

python3 {baseDir}/scripts/search_papers.py "machine learning transformers"

Search with filters:

python3 {baseDir}/scripts/search_papers.py "deep learning" --limit 5 --year 2020-2023 --min-citations 10

Search Options

--limit N: Number of results (default: 10, max: 100)
--year YYYY-YYYY: Filter by year range (e.g., "2020-2023" or "2023")
--min-citations N: Minimum citation count
--json: Output in JSON format for machine processing

Get Paper Details

Retrieve detailed information about a specific paper:

python3 {baseDir}/scripts/search_papers.py --details <paper-id>

Returned Data

Each paper includes:

title: Paper title
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authors: List of authors with names
year: Publication year
venue: Journal or conference name
citationCount: Number of citations
abstract: Paper abstract
url: Link to Semantic Scholar page
openAccessPdf: Direct PDF link if available
paperId: Unique Semantic Scholar ID (for details lookup)

Examples

Search for recent AI papers:

python3 {baseDir}/scripts/search_papers.py "large language models" --year 2022-2024 --limit 10

Find highly cited papers on a topic:

python3 {baseDir}/scripts/search_papers.py "quantum computing" --min-citations 50 --limit 10

Get JSON output for integration:

python3 {baseDir}/scripts/search_papers.py "neural networks" --json --limit 20

Tips

Use specific keywords for better results
Filter by year to get recent research
Use --min-citations to find influential papers
The API is free and requires no authentication
For complex queries, try multiple related terms

🤖 AI 评测

这个工具用起来还不错，能快速搜索学术论文，结果来自可靠的学术数据库，不需要注册就能用，还支持按年份、引用数等条件筛选。但它是通过命令行操作的，对普通用户来说不够直观，参数设置有点复杂。另外，它叫"Google Scholar Search"但实际用的是另一个数据库的名字，容易让人混淆。搜索结果只给摘要，没有全文预览，评估论文时不够方便。总体适合有学术需求的用户使用，但界面设计还有改进空间。