This study explored a new method to choose optimal parameters for support vector machine regression with RBF nucleus (RBF-SVR) and its application on the estimation of moisture content,volatile solid (VS) and the ratio of carbon to nitrogen (C/N) in animal manure compost products using near-infrared reflectance spectroscopy (NIRS). The efficiency of RBF-SVR method was compared with partial least-squares regression (PLSR) mainly using the determination coefficient of prediction (r2) of the standard error of prediction (SEP) and ratio of porformance to standard deviation [RPD (SD/SEP)]. In this study,120 commercial animal manure compost samples were collected from 22 provinces in China. Spectra of the orient samples were scanned with a SPECTRUM ONE NTS from 4000~10000 cm-1,respectively. Results showed stepwise search for optimal parameters was a feasible method for RBF-SVR. The efficiency of RBF-SVR method for moisture content,VS and C/N were all better than PLSR. Robust models using RBF-SVR were developed for moisture content and VS (r2>0.90,RPD>4.0) and for C/N (r2>0.85,RPD>2.5),respectively. Results showed the potential of NIRS with RBF-SVR to evaluate the products quality of animal manure compost,but further research would be needed for the higher precision.