A Study of Data Collection Techniques Based on Selenium and Python

Aiming at the limitations of traditional static crawlers in dynamic web page processing, this paper proposes a distributed intelligent data collection system based on Selenium and Python. By constructing a three-dimensional intelligent waiting strategy (network-element-time dimension), an elastic browser pool architecture, and a four-dimensional behavioral fingerprinting camouflage technique, the system effectively solves the problem of dynamic rendering content capture. Experiments show that the system has a capture success rate of 98.7% on benchmark datasets such as Google Cluster (58.4% higher than that of Scrapy), and the processing speed reaches 4,500 items/minute under the 5-node distributed architecture. Core innovations include: 1) multi-dimensional intelligent waiting strategy reduces the average waiting time by 40%; 2) browser pool technology supporting elastic expansion improves the memory utilization rate by 28.9%; 3) five-element data model realizes the unified representation of heterogeneous data from multiple sources, and the fusion efficiency is improved by 75%; 4) behavioral fingerprinting camouflage technology reduces the anti-crawling blocking rate to 0.8%. Multi-scenario validation shows that the system performs well in complex scenarios such as patent data (97.5% accuracy), e-commerce reviews (collection speed of 200 items/minute), and social media (multimodal support rate of 97.8%). The research provides an efficient and reliable technical solution for dynamic data collection, which has important application value in the fields of business intelligence, public opinion monitoring and academic research.