Affect-biased attention is an important predictive factor of children's early socio-emotional development, possibly shaped by the family environment. Our study aimed to reveal children's temporal dynamic patterns of affect-biased attention by looking at time series of attention to emotional faces, individual differences in temporal dynamics, and their relations with parenting practices. Sixty Chinese children (27 girls; mean age: 7.92 ± 1.09 years) viewed emotional--neutral face pairs (angry, sad, and happy) for 3,000 ms while their eye movements were recorded. First, results showed that overall looking time rather than manual reaction time revealed affect-biased attention: children looked more at angry and happy faces than neutral faces, although they looked at sad and neutral faces approximately the same amount of time. Temporal course analysis revealed further differences in visual attention to emotional faces: attention bias to emotional faces emerged early after the stimuli onset (before 400 ms), even for sad faces. This bias did not hold for the entire stimulus presentation time, and only the attention bias to happy faces appeared again in the later period. Second, we applied a data-driven cluster approach to the time series of attention to emotional faces and revealed three subgroups of dynamic affect-biased attention. Finally, the machine learning method revealed that positive parenting was related to the temporal dynamic patterns of children's attention to sad faces.