## 3 Materials and Method  
### Dataset Collection  
The dataset utilized in this study was collected from major e-commerce platforms across multiple representative industries over a continuous period from January 2023 to December 2023. The data exhibit a well-defined time-series structure and encompasses diverse behavioral features. To ensure both the practical relevance and generalizability of the experimental results, four core types of economic behavior data were gathered: product pricing records, sales feedback, user browsing behavior, and inventory levels. Data sources included leading platforms such as JD (jd.com), Taobao (taobao.com), Suning (suning.com), and Vipshop (vip.com). For each category, between 200,000 and 400,000 complete records were collected, resulting in a total dataset size exceeding 1,000,000 entries. The data were collected at a daily granularity, enabling robust time-series modeling, as shown in Table1.  
During the data collection process, an automated scraping system was developed using Python 3.13-based tools, including Scrapy and Selenium. While these tools enabled the acquisition of large-scale behavior data from multiple e-commerce platforms, their use also introduced potential risks, such as parsing errors caused by frequent website structure changes, anti-scraping mechanisms, and inconsistent page templates. To mitigate these issues, several safeguards were employed: structural schema validation was applied during crawling to ensure field consistency, periodic manual sampling was conducted to audit data quality, and records with missing or anomalous key attributes were filtered out during preprocessing. Additionally, data collection was performed within a constrained time window to minimize discrepancies due to platform-side updates. For the pricing records, data were periodically retrieved from product detail pages and promotional listings, capturing listed prices, discount prices, and associated timestamps. The pricing fields followed a consistent structure, incorporating price fluctuation ranges, update frequency,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data Type** & **Source Platforms** & **Number of Records** & **Collection Period** \\ \hline Product Pricing Records & JD, Taobao, Suning & 205,890 & April–December 2023 \\ Sales Feedback & Taobao, Suning, Vipshop & 280,312 & July–December 2023 \\ User Browsing Behavior & Taobao, Suning & 279,034 & January–June 2023 \\ Inventory Levels & JD, Taobao & 348,910 & February–October 2023 \\ \hline
**Total** & — & **1,000,000+** & **Daily Time Series** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Behavior data subtype statistics.  
and promotional tags, which effectively reflected the dynamic characteristics of pricing strategies. Sales feedback data were obtained from historical transaction statistics and monthly sales rankings, with fields including daily sales volume, cumulative monthly sales, and sales ranks, normalized using timestamp alignment for consistent analysis. Browsing behavior data were extracted from page popularity indicators, click-tracking modules, and redirection logs in recommendation zones. The collected fields included click frequency, navigation depth, and average session duration, providing insight into user engagement patterns. Inventory data were inferred from stock status prompts, restock frequency indicators, and seller dispatch cycles on product pages, and further refined through integration with pricing and sales information to approximate real-world supply-demand dynamics.