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A new kind of neural-based market prediction computing approach for electronic commerce has been presented by our research group in this paper. Our purpose is to suggest a successful and efficient prediction method of risk assessment for the clients to get substantial profits in competitive commerce markets of electronic commerce. In this paper we study the benefits of combining two layered feed forward neural networks trained by back propagation on an identical data set. In this case, network diversity was achieved by the inherent randomness associated with the back-propagation algorithm's initialization of a network's weights. By case experiments of risk assessment of electronic commerce, our technique has been tested effectively.