The rubber antioxidant 6PPD and its toxic quinone transformation product 6PPD-Q are emerging aquatic contaminants with fragmented global occurrence data. This study constructed a full-process data mining framework integrating literature retrieval, lexical matching, attention-augmented neural network modelling and tabular extraction to compile 6PPD/Q records from 6360 Web of Science papers, achieving 85%-95% entity recognition accuracy. The compiled dataset revealed a pronounced mid-latitude enrichment pattern (30° N-45° N) correlated with population density and traffic intensity, with ultra-high hotspots in Los Angeles runoff (up to 6.10 μg/L) and Lake Sihwa sediment (330 ng/g). Toxicity assessment across 26 species confirmed 6PPD-Q is far more acutely toxic than 6PPD, especially to salmonids. Global risk quotient mapping consistently locked ecological hotspots to the Northern-Hemisphere mid-latitude belt, with the North-American west coast as the highest-risk zone. This work provides a reusable framework and the most comprehensive global 6PPD/Q dataset to support regulatory formulation and ecological risk mitigation.