| Summary: | Given the complex influence of various factors on soil nitrogen (N) and phosphorus (P) loss through runoff in a karst environment, analyzing the importance of different factors to determine the most efficient method for soil nutrient conservation remains a key challenge. Herein, we proposed a novel intelligent analysis strategy based on the Random Forest (RF) regression algorithm to identify the main features and discover the fundamental mechanisms among them under a rock-exposed karst slope with synchronous existence of surface runoff and subsurface leakage. Typically, the results indicated that the rock–soil angle (β) was the main factor influencing soil N and P loss, which was further confirmed based on the RF regression-multifactor analysis. The proposed strategy was used to characterize the relationships of inflow rate, soil bed–ground angle, and rock–soil angle with soil N and P concentrations in soil surface runoff, subsurface runoff, and fissure runoff to study the potential application of soil N and P loss under karst conditions. Our results provide a new approach and promising potential for soil nutrient conservation and related soil and plant research.
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