E-commerce Price Scraping

This paper systematically analyzes the core technical implementation path of e-commerce price crawling, covering key links such as dynamic rendering processing, anti-crawler strategy breakthrough, and heterogeneous data standardization, and proposes a high-availability system architecture design in combination with the abcproxy proxy service.

1. Design of technical architecture for e-commerce price crawling

1.1 Dynamic page rendering processing

Headless browser control: Use Puppeteer/Playwright to simulate user operations and trigger JavaScript dynamic loading of price data

Intelligent analysis of DOM structure: A hybrid positioning strategy based on XPath and CSS selectors to cope with page structure adjustments (tolerance rate > 92%)

Rendering wait strategy: Set up a dynamic loading detection mechanism to determine the data readiness status through element existence checks

1.2 Anti-crawler system

Traffic characteristics simulation:

Request header randomization: dynamically generate 12 HTTP header fields such as User-proxy and Accept-Language

Mouse trajectory modeling: Generate human operation mode that conforms to Bezier curve (average movement speed 120px/s±15%)

IP Rotation System:

Configure abcproxy residential proxy pool to achieve automatic switching of request IP according to rules (change rate ≥ 80% per minute)

Integrate IP health detection API to automatically isolate nodes blocked by target websites

1.3 Data cleaning and standardization

Price information extraction:

Regular expression to match multi-currency price formats (e.g. \$12.34, €12,34)

Handling the nested structure of promotional prices and strikethrough prices (CSS pseudo-element content extraction)

Attribute association mapping:

Construct SKU feature matrix to associate product specification parameters with price fluctuations

Implemented a multi-platform product ID mapping system (matching accuracy > 85%)

2. High-concurrency crawling system optimization solution

2.1 Distributed Architecture Design

The crawler nodes are deployed in Master-Worker mode, and the task scheduling granularity is accurate to the commodity classification level.

Use Redis to implement distributed queue management, supporting horizontal expansion to 100+ node clusters

2.2 Intelligent speed limit algorithm

Dynamically adjust request frequency based on target website response time (baseline value: 200ms/request)

Introducing reinforcement learning models to predict website load thresholds and circumvent access rate limits

2.3 Cache reuse mechanism

Establish a time series database of price data and implement an incremental capture strategy for stable commodities

Define the sensitivity level of commodity price fluctuations and dynamically adjust the monitoring frequency (the crawling interval of high-frequency categories is compressed to 15 minutes)

3. Technical implementation plan for typical scenarios

3.1 Cross-border e-commerce price and tax monitoring

Technology stack combination:

Multi-language page rendering (Selenium Grid cluster)

Real-time exchange rate conversion interface integration

Data Dimensions:

Capture tax-inclusive price, freight, and tariff calculation rules

Monitor the validity period of cross-border exclusive promotions

3.2 Competitive Product Price Early Warning System

Feature Engineering:

Construct price change rate indicator (daily fluctuation > 5% triggers warning)

Identify hidden price reduction strategies (detection of disguised price reduction through gifts)

Response mechanism:

Automatically generate price adjustment recommendation reports

Recommend the best time to adjust prices based on relevant inventory data

3.3 Historical Price Analysis Modeling

Data Application:

Training LSTM model to predict commodity price trends (fit R²>0.78)

Constructing a price elasticity coefficient matrix to guide sales promotion strategies

Visualization:

Generate price heatmaps to show regional pricing differences

Drawing a price fluctuation correlation network diagram reveals the relationship between competing products

4. Legal compliance and ethical risk control

4.1 Data Collection Boundary Management

Comply with the target website's robots.txt protocol and set whitelist domain name restrictions

Implement data desensitization and automatically filter personal information in user reviews

4.2 Access Frequency Compliance

Prioritize the public API of the docking website and reduce the proportion of page crawling

Set up geo-distributed latency policies to comply with access restrictions in different jurisdictions

4.3 Code of Business Ethics

Establish a price data use authorization verification mechanism

Avoid price manipulation through technical means

5. Technological evolution and innovation direction

5.1 Breakthrough in Intelligent Analysis Technology

Developed a CNN-based page structure recognition model to automatically adapt to revisions (accuracy increased to 94%)

Use OCR technology to extract image price information and support screenshot price comparison function

5.2 Edge Computing Integration

Deploy lightweight crawling modules on CDN nodes to reduce network latency

Achieve distributed crawling of terminal devices (daily processing capacity exceeds 10 million requests)

5.3 Blockchain Evidence Storage Application

The captured data is stored on the chain to ensure that the price history cannot be tampered with

Build a decentralized price verification network to enhance data credibility

As a professional proxy service provider, abcproxy's residential proxy and data center proxy products can provide highly anonymous network access solutions for e-commerce price crawling. Through intelligent IP rotation and traffic feature camouflage technology, the continuity and accuracy of data collection are effectively guaranteed. If you need to build an enterprise-level price monitoring system, it is recommended to visit the abcproxy official website to obtain a customized proxy configuration plan.