1. A prediction method of near field photolithography line fabrication by using the combination of Taguchi method and neural network, comprising: a first step of combining a theoretical model and the experiment of a near field photolithography line fabrication, using a non-destruction method to back calculate the fiber probe aperture size, setting the error between an experimentally measured result and a simulated result of the theoretical model of near field photolithography as an objective function, and using the objective function, an optimizing search and a reasonable convergence rule to back calculate a fiber probe aperture size that matches the experimental and theoretical model; a second step of using an orthogonal array of the Taguchi method to perform a near field photolithography line fabrication experiment, using a L 9 orthogonal array to perform the near field photolithography line fabrication experiment so as to reduce experimental frequency; a third step of performing a data analysis of the Taguchi method, and producing a response table, a response chart and an analysis of variance (ANOVA) table; a fourth step of using the experimental data of the orthogonal array as training examples of a first stage network (ANN) to build a preliminary network, which also serves as a reference for improving the result of a second stage network and a third stage network; a fifth step of performing a second stage fine training of the network using light factors of Taguchi analysis as the training examples for the expansion of the second stage network and performing the second stage fine training, and increasing the frequency of the training examples of the experimental data to emphasize the importance of the information of the experimental data over the light factor data; a sixth step of determining third stage critical experimental training examples to be added according to the ‘important factors’, ‘uncertain factors’ and ‘preferred combination of factors according to ANN inferred Taguchi parameters’; and a seventh step of completing the third stage high prediction accuracy neural network to build a set of experimental prediction model that requires fewer experimental frequency, shorter prediction time and higher prediction accuracy.