Early Child Education (ECE) quality encompasses a complex, nonlinear system that includes both structural quality elements, like child-teacher ratio, as well as process quality features, such as teacher-child interactions. This study employed an artificial intelligence technology entitled Back-Propagation Neural Network (BPNN) to model the nonlinear relationship between these ECE quality elements. Based on the Guangdong Preschool Rating and Monitoring System, eighteen structural quality indicators were identified and examined in relation to process quality as measured by the Classroom Assessment Scoring System (CLASS). In addition to examine the nonlinear relationship between structure and process quality elements, the study also utilized a Mean Impact Value (MIV) analysis to identify the relative importance of each structural indicator for predicting the teacher-child interaction quality. Results showed that all eighteen structural indicators predicted teacher-child interaction quality to some extent in the nonlinear model. Moreover, the MIV analysis identified classroom size, child-teacher ratio, and teacher certification as the three highest contributors to explaining teacher-child interaction quality, whereas indicators about the space and facility showed the weakest relationship. These findings offer new insights into how ECE classroom quality elements work together to support effective classroom practice.