Analysis of Influencing Factors of High-Skilled Labor Based on Association Rule

High-skilled labor plays an important role in regional economic development, yet accurately identifying its influencing patterns remains challenging due to complex factor interactions, spatial spillover effects, and fuzzy boundaries among urban characteristics. Traditional regression-based approaches primarily focus on isolated linear effects, making it difficult to capture multi-factor combinatorial relationships underlying talent agglomeration. To address these limitations, this study proposes a spatially aware fuzzy association rule mining framework by integrating soft-gated spatial weighting and concept stability theory. Using data from the Sixth and Seventh National Population Censuses and the China City Statistical Yearbook, the framework is applied to the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Middle Reaches of the Yangtze River (MRYR) regions from 2010 to 2020. The results show that the associative patterns of high-skilled labor evolved substantially across regions. In the BTH region, dominant factors shifted from administrative hierarchy and environmental amenities to stronger interactions between economic growth and talent inflow. In the YRD region, economic dynamism gradually replaced static geographic advantages, while in the MRYR region, market-oriented drivers increasingly surpassed administrative-led resource concentration. Overall, the findings suggest a transition from single-factor dependence to multi-factor coupled patterns in China’s regional talent agglomeration.