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Parallel Genetic Algorithms (PGAs) were used to simultaneously optimize the structure and weights for feedforward neural networks. Aiming at its large-scale application in common distributed network system, a concentrative coarse-grained model for parallel evolutionary neural networks is designed and realized in a laboratorial distributed computation environment, and the initial results of experiments indicate that the parallel model can quicken the searching process and improve the evolutionary efficiency. For the parallel characteristics, this method can analyze massive information during the process of e-business. Several application scopes of this method in e-business are discussed also in this paper.