Numerical treatment of synthetic characteristic curves plays an important role in the selection of turbine types, optimal operation of hydropower plants, and computational simulations, different numerical treatments may result in different outcomes. Because the differences of the outcomes and their impacts on the correlations among the parameters are unknown, this may cause the gap between the actual operation and the predictions, thus influencing the safe and economical operation of the turbine. In order to compare the differences and applicability of different numerical methods, we used A858a-36.6 turbine as the research object and adopted high order surface fitting, three dimensional spatial surface interpolation, and BP neural network fitting to treat the model characteristic curve respectively. Then the simulation results were judged by the sum of the relative error between the simulation results and the actual operation data. According to the comparison results, these three methods can all be used to deal with the curve, but high order surface fitting method performs poorly in accuracy, so it is only suitable for rough solutions of the correlations among parameters in a short time. Three dimensional spatial surface interpolation gets the highest precision, which is suitable for data processing of the pure image type or visualization problems. The precision of BP neural network fitting method is between these two methods, it can be used as a supplement for three dimensional spatial surface fitting. What’s more, the more samples it gets, the higher the precision becomes.