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Floods occur frequently and cause considerable damage to local environments. Effectively assessing the flood risk contributes to reducing loss caused by such disasters. In this study, the weighted naïve Bayes (WNB) method was selected to evaluate flood risk, and the entropy weight method was employed to compute the weights. A sampling and verifying model was employed to generate the most accurate conditional probability table (MACPT) to calculate the probability of flooding. When using the framework integrating WNB with the sampling and verifying model, previous studies could not obtain a WNB-based MACPT and the WNB classification accuracy, for lacking WNB functions that could be called directly. Facing this issue, in this study we developed WNB functions with the MATLAB platform to directly integrate with the sampling and verifying model to generate a WNB-based MACPT, contributing to the greater interpretability and extensibility of the model. Shantou and Jieyang cities in China were selected as the study area. The results demonstrate that: (1) a WNB-based MACPT can reflect the real spatial distribution of flood risk and (2) the WNB outperform the NB when integrated with the sampling and verifying model. The resulting gridded estimation reveal a detailed spatial pattern of flood risk, which can serve as a realistic reference for decision making related to floods. Furthermore, the proposed method uses less data, which would be helpful in developing countries where long-term intensive hydrologic monitoring is limited.