Abstract: Abstract: Varieties of risk factors such as operating environment, personnel, equipment may bring microbiological hazards into foods. In order to effectively implement risk management，we used the modular process risk modeling framework to improve the quantitative microbiological risk assessment method. In this method, risk factors were abstracted as a hazard transfer modular. Food safety risk factors included the sources of raw materials, storage risk, operational personnel hygiene, and environmental contamination risks. The hazard transfer process described the contamination frequency and the probability distribution of microbial quantity that introduced into a product by environment, operating personnel, equipment and other risk factors. If risk did not exist, the amount of microbial number was zero. If risk existed, the probability distribution of microbial number was defined with discrete or continuous distribution function. The proportion transferred to a product was described by function g(?) with operating time, temperature, contact area and etc. The control process described varieties of control measures such as the use of different disinfection, the implementation of different test frequencies may be taken in the production. The prevention and control impacts of a measure were described by the probability and quantity change of microbial number introduced by risk factors in the model. The utility modular was used to characterize the consumption cost and gain of the control measure. A modular process risk model can be established by Bayesian network by the following three steps: 1) Define processes, materials mixing and partitioning, and processing parameters, select risk factors that may introduce microbes into a product and affect the microbial dynamics; 2) Select the appropriate basic processes, and define the Bayesian network structure of risk model; 3) Collect risk data, and define the conditional probability of each node in the model by analyzing risk data. Combined with predictive microbiology, the number and occurrence probability of microbiological hazards in each process can be estimated using Bayesian inference. This risk model is capable of assessing the risk situation and the impact of risk factors on food safety. It also can trace the source of microbial hazards. Once adding control processes to hazard transfer processes, and modifying the conditional probability of the corresponding hazard transfer processes nodes, the effectiveness of one or several control measures can be verified. In the model, parameters of nodes can be adjusted to make it easy to assess the impact degree of risk factors and control measures on food product safety. Using this improved modeling method, the origin of microbiology hazard can be traced. Through comparing the effectiveness and profit of different control measures preferred one or control combination can be determined.