Abstract: Mastering hydraulic conductivity of rock mass is an important way to precisely describe hydrogeological characteristics of a certain region. Hydraulic conductivity is a significant indicator to reflect the rock mass’ permeability. The studies of hydraulic conductivity estimation models have important implications for the development of actual engineering. In the existing estimation models of hydraulic conductivity, the single-factor model cannot take into consideration of the comprehensive influence of various factors on hydraulic conductivity in the area, and the parameters selection of the multi-factor model lacks the flexibility and its application is limited when some parameters are difficult to be obtained, etc. The classification and comparative analyses of the positive and negative correlation parameters are conducted based on public data. We propose a set of high-fitting hydraulic conductivity estimation models, which are the PNC (Positive and negative correlation) model. The research results show that the fitting result of the PNC model (R²=0.964 and R²=0.801) is superior to that of the HC model (R²=0.905 and R²=0.563) in No. 1 study area. In No. 2 study area, the fitting result of the PNC model (R²=0.959) is superior to that of the RMP model (R²=0.927). In No. 3 study area, the fitting result of the PNC model (R²=0.94 to 0.99) is also better than that of the ZRF model (R²=0.92 to 0.99). By using Nash-Sutcliffe coefficient (NSE) to carry out the error analyses of the model, it is found that the error coefficients of 5 in 7 sets of data are above 0.95. It further illustrates the convenience and reliability of the PNC model, which can provide a certain reference for estimating and verifying hydraulic conductivity in actual engineering.