In general, profit forecasting is a difficult task as it is affected by a group of indicators which descript the conditions of the external and internal environment of the company. Holistic overview of the current literature indicates that profit forecasting methods are mainly divided into two categories. One is statistical methods, such as regression model (RM), time series analysis, and gray system theory, another is neural network (NN) method. However, little attention has been directed to network graph (NG) method. To address these problems, we define NG to describe the non-linear relationship between the company's net profitability and its external and internal environment factors, and develop the enhanced community mining method (ECMM) based on Bayesian method (BM) for company profit predicting. ECMM successfully models the complex nonlinearity and gives better results than the traditional RMs and NN methods because ECMM can set flexible coefficients for different values of the independent variable and adopt complex nonlinear function to map the relationship between the independent variables and dependent variable. In ECMM, BM is adopted to express the clustering rules of NG’s nodes. Furthermore, the improved bacterial foraging optimization algorithm (IBFOA) is developed to calculate Bayesian probability function (BPF) as well as to optimize their parameters. IBFOA is distinctive because it generates new solution according to the statistical analysis of the existing solutions and adaptively adjusts its parameters, those can increase the diversity of the population and avoid the prematurity. Last but not least, we use bi-objective method to assess the accuracy of prediction and Pareto boundary method (PBM) is created to integrate the two indicators into one objective function, as a result the reliability of the valuation model is improved.