Based on the hydrological process, several factors affecting the mean soil infiltration rate (im) under the individual rainfall event were determined, which were the slope gradient (S), slope length (L), rainfall intensity (Ri), rainfall amount (Rain), vegetation cover of the land surface (Vc), antecedent soil water content (Asw) and fractal dimension of soil particle (D). Using the data obtained from the field runoff-plot under natural rainfall events, the quantitative relationships between im and the seven factors were analyzed, and the multi-parameter estimation model for im was established by means of multivariate nonlinear regression method and BP neural network model. Relationship between im and S was in accord with quadratic parabola, and im was firstly increased and then decreased with increase of S. The im was increased linearly with the increase of L and Ri, it was increased with the increase of Rain by power function and linearly decreased with increase of D. Hyperbolic functions were obtained between im and Vc, Asw, and the im was increased with increase of Vc and decreased with increase of Asw. On the strength of the seven functional relationships, the estimation model of im was built by multivariate nonlinear regression method. The relative error of around 72% data was within ±10%. Using the seven factors as input parameters, a BP neural network model for prediction of im was established. The best training algorithm was Levenberg-Marquardt method and the ideal neurons nodes of the hidden layer were determined as 15 by the grey relational degree method. The relative error of around 81% data was within ±10%.