Uncertainty during the period of software project development often brings huge risks to contractors and clients. Developing an effective method to predict the cost and quality of software projects based on facts such as project characteristics and two-side cooperation capability at the beginning of the project can aid us in finding ways to reduce the risks. Bayesian Belief Network (BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table. In this paper, we build up the network structure by Delphi method for conditional probability table learning, and learn to update the probability table and confidence levels of the nodes continuously according to application cases, which would subsequently make the evaluation network to have learning abilities, and to evaluate the software development risks in organizations more accurately. This paper also introduces the EM algorithm to enhance the ability in producing hidden nodes caused by variant software projects.