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A risk evaluation model in software project investment based on Bayesian networks (BNs) is presented in this paper. The BNs parameter learning is applied to the modeling process based on sample data set, so that the BNs is more accordant with the project feature in the software project investment phase. In addition, the validity of the parameter learning is validated with algorithm precision and convergence. Practice proves that the risk evaluation model can provide the accurate risk information for decision-makers in the process of software project investment.