# Biketo China Cycling News & Product Scraper (`jungle_synthesizer/biketo-china-cycling-news-product-scraper`) Actor Scrapes Biketo (美骑网) — China's largest cycling portal — for news, product reviews, and race coverage since 2008. Enumerates articles by sequential ID across three channels. Returns title, author, publish date, channel, body text, lead image, and engagement metrics. - **URL**: https://apify.com/jungle\_synthesizer/biketo-china-cycling-news-product-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** News, Sports - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Biketo China Cycling News & Product Scraper Scrapes **Biketo (美骑网)** — China's largest and longest-running cycling portal — for news articles, product reviews, and race coverage. The site has published continuously since 2008, accumulating over 56,000 articles across three channels. This actor enumerates the complete back-catalog using Biketo's sequential article ID scheme, making it ideal for building a Mandarin cycling corpus for LLM fine-tuning, market research, or trend detection. ### What you get Each scraped record contains: | Field | Description | |-------|-------------| | `articleId` | Numeric article ID (e.g. 56323) | | `articleUrl` | Full canonical URL | | `channel` | Biketo's channel label in Chinese (e.g. 美骑快讯, 产品快讯, 赛事新闻) | | `title` | Article headline in Chinese | | `tags` | Comma-separated category tags from the article header | | `author` | Author or source attribution | | `publishDate` | Publish date-time (YYYY-MM-DD HH:MM:SS) | | `leadImage` | URL of the first image in the article body | | `bodyText` | Full article body text, whitespace-collapsed | | `viewCount` | Page view count (integer) | | `commentCount` | Comment count (integer) | | `scrapedAt` | ISO-8601 scrape timestamp | ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `startId` | integer | 1 | Article ID to start enumeration from | | `endId` | integer | 56500 | Article ID to stop at (inclusive) | | `channels` | array | `["news","product","racing"]` | Content channels to include | | `maxItems` | integer | — | Cap on total articles to return | #### Content channels - **news** — Cycling news, industry coverage, product announcements (`/news/.html`) - **product** — Gear reviews and product features (`/product/.html`) - **racing** — Race coverage and results (`/racing/.html`) All three channels share the same sequential ID space. IDs are enumerated in parallel across selected channels; invalid IDs for a given channel are silently skipped. ### Usage examples **Full back-catalog (all channels, ~56k articles):** ```json { "startId": 1, "endId": 56500, "channels": ["news", "product", "racing"] } ```` **Recent articles only (incremental update):** ```json { "startId": 56200, "endId": 56500, "channels": ["news", "product", "racing"], "maxItems": 100 } ``` **Product reviews only:** ```json { "startId": 1, "endId": 56500, "channels": ["product"] } ``` ### Notes - **Charset**: Biketo serves pages in GB2312. The actor transparently decodes to UTF-8 via Crawlee's built-in charset handling — all output fields are clean UTF-8 Chinese text. - **Rate limiting**: The actor uses moderate concurrency (5–15) with polite crawling. No proxy is required; the site is fully accessible to datacenter IPs. - **Invalid IDs**: Not every ID exists in every channel. The actor skips URLs that return 404 or lack an article heading — no error is logged for these, keeping run logs clean. - **Resumability**: For large runs, set `startId` and `endId` to narrow ranges. Re-run with updated `startId` for incremental updates. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `startId` (type: `integer`): Sequential article ID to start enumeration from (e.g. 1 for full catalog, or a recent ID for incremental runs) ## `endId` (type: `integer`): Sequential article ID to stop at (inclusive). Latest ID is around 56,500. Use with startId for date-range scraping. ## `channels` (type: `array`): Which Biketo content channels to scrape. 'news' = cycling news articles, 'product' = gear/product reviews, 'racing' = race coverage. ## `maxItems` (type: `integer`): Maximum number of articles to scrape across all selected channels. Useful for test runs. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 56300, "endId": 56325, "channels": [ "news", "product", "racing" ], "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 56300, "endId": 56325, "channels": [ "news", "product", "racing" ], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/biketo-china-cycling-news-product-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 56300, "endId": 56325, "channels": [ "news", "product", "racing", ], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/biketo-china-cycling-news-product-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 56300, "endId": 56325, "channels": [ "news", "product", "racing" ], "maxItems": 10 }' | apify call jungle_synthesizer/biketo-china-cycling-news-product-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/biketo-china-cycling-news-product-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Biketo China Cycling News & Product Scraper", "description": "Scrapes Biketo (美骑网) — China's largest cycling portal — for news, product reviews, and race coverage since 2008. Enumerates articles by sequential ID across three channels. Returns title, author, publish date, channel, body text, lead image, and engagement metrics.", "version": "0.1", "x-build-id": "cvQFjisqlLdHf4fF9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~biketo-china-cycling-news-product-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-biketo-china-cycling-news-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~biketo-china-cycling-news-product-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-biketo-china-cycling-news-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~biketo-china-cycling-news-product-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-biketo-china-cycling-news-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "startId", "endId", "channels" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "startId": { "title": "Start Article ID", "type": "integer", "description": "Sequential article ID to start enumeration from (e.g. 1 for full catalog, or a recent ID for incremental runs)", "default": 1 }, "endId": { "title": "End Article ID", "type": "integer", "description": "Sequential article ID to stop at (inclusive). Latest ID is around 56,500. Use with startId for date-range scraping.", "default": 56500 }, "channels": { "title": "Content Channels", "type": "array", "description": "Which Biketo content channels to scrape. 'news' = cycling news articles, 'product' = gear/product reviews, 'racing' = race coverage.", "default": [ "news", "product", "racing" ], "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of articles to scrape across all selected channels. Useful for test runs." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```