Humans spend a considerable amount of time indoors, and indoor biological airborne pollutants may harm human health. Active bioaerosol samplers and conventional microbiological culture methods, which are widely applied in studies of airborne microbial contamination, are not only unable to perform continuous monitoring over long periods, but are also time-consuming and expensive. In order to rapid assess indoor airborne microbial contamination, multiple linear regression models were constructed by statistically analyzing the measured bioaerosol samples and the real-time measured mass and number concentrations of airborne particles using a direct reading instrument from 43 air-conditioned public spaces. There were significant positive correlations of indoor airborne bacterial and fungal concentrations with indoor sizesegregated particle mass and number concentrations. The predictive power of the model was sufficient for predicting indoor bacterial concentrations from the indoor and outdoor size-segregated particle number concentrations as independent variables. Particle number concentration outperforms particle mass concentration as an independent variable in predicting indoor bioaerosol concentrations. The prediction model for indoor bacterial bioaerosol levels constructed in this study could facilitate a rapid assessment of potential airborne bacterial contamination via the simple and feasible measurement of particle number concentration, thus helping to improve the management and maintenance of indoor air quality.