This paper uses the Poisson distribution with a linear combination of Gamma distributions to capture the dependence among the default indicators of different assets, and then proposes a multi-factor risk model for portfolios credit risk based on mixed Poisson default intensity.Our model is based on the idea that dependence among common risk factors can be transformed into the dependency among the default indicators of different assets, which broadens and enriches portfolio credit risk measurement models.By introducing important sampling techniques to the model for effective numerical simulation, this paper empirically examines the portfolio credit risk in four industries of China’s financial markets.More specifically, the classic structural model and option pricing formula are firstly used to estimate the dynamic default probability of an obligor;the dynamic Poisson strength of each asset under the mixed Poisson model is secondly obtained by using the dynamic default probability of a single asset;then, the factor loading coefficients of common risk factors are estimated, to reflect the degree of dependence among different assets;finally, the important sampling method is applied into the mixed Poisson model, in order to implement the efficient Monte Carlo simulation for the loss distribution of the credit portfolio composed across different industries.The simulation results show that our algorithm is more efficient than the ordinary Monte Carlo simulation and can greatly reduce the variance of the estimated loss probability.