To predict the amount of nitrate nitrogen loss efficiently, the characteristics and influencing factors of nitrate nitrogen in slope land were studied by analyzing literature data of simulated rainfall experiments. RUSLE model was used to estimate nitrate nitrogen loss. Based on quantitative analysis of influencing factors and loss of nitrate nitrogen, an empirical formula of nitrate nitrogen loss with surface runoff was established, and the accuracy of the formula was verified by literature experiment data. The results indicated that the values of R, erosive force of rainfall, ranged in 200～3 220 (MJ·mm)/(hm2·h) and mainly distributed in 500～2 100 (MJ·mm)/(hm2·h), R factor was a reflection of potential soil erosion caused by rainfall. Soil erodibility was an important index to evaluate soil sensitivity and was affected by soil properties, the values of soil erodibility factor K were distributed between 0.007 to 0.095 (t·hm2·h)/(hm2·MJ·mm). Vegetation cover and management factor could be easily controlled to alleviate soil erosion and nutrient loss, the values of C factor were distributed between 0.006～0.930. Water conservation measures was a limiting factor of soil and water conservation, the ranges of water conservation measures factor P were 0.08～0.81. The values of factor P in different conservation measures showed that engineering mode＜comprehensive mode＜cultivation mode＜grass mode. Hence, engineering mode was the most efficient measure to reduce soil and water loss. The amounts of nitrate nitrogen loss were positively related with each influencing factor, which could be described by power function. In order to verify the accuracy of the empirical formula, six rainfall events collected from the previous researches, which haven’t been used in assessment of parameters. The relative error between calculated values and measured values of nitrate nitrogen loss amount with surface runoff was 30.28% and the model certainty coefficient was 0.772. The predicted formula developed in this study could accurately predict nitrate nitrogen loss because of the calculated data concur with the measured data. Further, these findings provided a theoretical basis for predicting nitrate nitrogen loss and optimizing the control measures of prevent nitrate nitrogen loss from rainfall events.