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To achieve rapid monitoring of spring maize growth and gain realtime understanding of field crop conditions, focusing on spring maize planted in the Karamay region of Xinjiang, utilizing UAV multispectral imagery for growth monitoring of the spring maize, based on groundcollected data on spring maize leaf chlorophyll content, leaf area index, aboveground biomass, and plant height, comprehensive growth indicators CGMIEWM and CGMIFCE were established by combining the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE). Spectral indices were constructed by using UAV remote sensing imagery data, and the optimal input variables for the model were determined by using Pearson correlation analysis and the variance inflation factor. Partial least squares (PLS), random forest regression (RF), and particle swarm optimization (PSO) were used to optimize the RF model and establish a spring maize growth inversion model. By combining model accuracy evaluation metrics, the spatial distribution map of spring maize growth was ultimately determined. The results showed that the comprehensive growth indicators constructed using CGMIEWM and CGMIFCE had higher correlations than single growth indicators. The growth indicators derived from CGMIFCE, combined with the PSO-RF model, resulted in the best performance for inversion of spring maize growth. The coefficient of determination (R2) was 0.823, the root mean square error (RMSE) was 0.084%, and the relative percent deviation (RPD) was 2.345. The growth of spring maize in the study area was mostly concentrated in the normal growth (ZZ) category, indicating relatively stable growth across the region. The research results can provide a scientific basis for the field management of spring maize and offer a data foundation for the development of precision agriculture.