
By entering a URL, batch extract public profile data, including channel name, subscriber count, video count, view count, description, popular videos, etc., outputting in CSV or JSON format. Supports competitor analysis, user research, zero-code operation, one-click export of structured data.
YouTube Channel Scraper is a data extraction tool designed to batch collect channel name, subscriber count, video count, view count, description, popular videos, and other data from public YouTube profiles via URL. Using CoreClaw, you can obtain structured data with zero coding, helping with competitor analysis and user research.
| 📺 Channel Name and ID | 🔗 Channel Link |
|---|---|
| 👥 Subscriber Count | 🎬 Total Videos |
| 👀 Total View Count | 📝 Channel Description |
| 🖼️ Channel Banner and Avatar | 📅 Registration Date |
| 🌍 Country/Region | 🔗 Third-Party Links |
CoreClaw YouTube Channel Scraper handles proxy rotation, task scheduling, data standardization, and final delivery for you in the background. In just a few minutes, you can obtain data through the following steps:
Enter YouTube channel link.
Example: https://www.youtube.com/@mrbeast
(Dashboard input image example)
For your convenience, output results are displayed in tables and tabs. You can choose to download results in CSV/JSON format. Here is an example of channel data scraped using channel URL:
(Dashboard output image example)
(JSON output example)
If you need to scrape other types of data, CoreClaw offers the following professional toolsets optimized for different scraping scenarios.
The tool will scrape complete public data from the channel, including all basic information and popular video list. Data volume depends on the completeness of the channel's public information.
The tool supports detailed error feedback and intelligent retry mechanisms. When scraping anomalies occur, the service will return a response containing error codes and detailed prompt information. For temporary errors, the service defaults to automatic retry.
Evaluate channel influence through the following data dimensions:
Channel data is very suitable for competitor analysis applications:
Yes. Supports batch input of multiple channel URLs to scrape complete data from multiple channels at once, facilitating horizontal comparison and analysis.
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