The fuzzy point estimate method can simultaneously consider fuzzy and random uncertainty in slope stability analysis. To overcome the drawbacks of a great deal of computing existing in traditional fuzzy point estimate method, an improved fuzzy point estimate method is proposed on the basis of artificial neural network. Firstly, the Latin hypercube sampling method and radial basis function (RBF) neural network are adopted to establish a prediction model for determining safety factor of slopes. Secondly, fuzzy-random variables, i.e. cohesion and friction angle, are transformed into interval numbers by the λ cut set approach, and then combined at each cut set level. The corresponding safety factor of each variable combination is obtained with the established prediction model. Finally, reliability index of slope is calculated using the point estimate method. A practical example is analyzed, showing that the proposed method is convenient and reliable to evaluate slope stability, and can be further improved to increase the computational accuracy of slope reliability index by increasing the number of λ cut set levels. For those slopes with 2-4 fuzzy-random variables, as the proposed method is used to compute the reliabilty of a slope, the number of λ cut set levels is recommended to be 25.