Field or laboratory measurements are commonly conducted to determine phthalates concentrations in spaces. This study investigated the association between various influencing factors and indoor phthalates concentrations. Back-propagation (BP) Neural Network was employed to verify a prediction model of indoor phthalates concentration with 80% of experimental data and 20% remaining data. The validation of remaining data shows a reasonable accuracy for model application, where the ratios of standard deviations were all greater than 0.45, most E_RMS were close to 0 and all the E_MR were less than 15.5%. In addition, we used relevant data from the China, Children, Homes, Health (CCHH) study conducted in Tianjin for further inspection. The prediction on di (2-ethylhexyl) phthalate (DEHP) concentration was performed, which indicated a high accuracy. Furthermore, the Monte-Carlo simulation was applied to quantify the effect of temperature on phthalates concentration combining with the prediction model. When the temperature increment value was 4°C, the average relative decrease ratio of dibutyl phthalate (DBP), DEHP, diisobutyl phthalate (DIBP) concentration was about 7.8%, 12.8% and 9.3%, respectively. The findings have established the validity of the prediction model and provided a quantification of the influence of temperature on the concentration of phthalates in the indoor dust phase.