The use of multispectral technology to carry out chlorophyll relative content (SPAD) monitoring of Cinnamomum camphora dwarf forest could provide timely diagnosis of Cinnamomum camphora dwarf forest growth and provide timely information support for field management decisions. The SPAD inversion model of Cinnamomum camphora dwarf forest was constructed by using UAV multispectral remote sensing images to extract band reflectance and filter vegetation index, which took band reflectance and vegetation index as model input respectively, and four methods were used: partial least squares regression (PLSR), support vector regression (SVR), back propagation (BP) neural network and radial basis function (RBF) neural network, and different input quantities and the inversion accuracy of simulation results of different models were compared. The results showed that there was little difference in the accuracy of inversion in the same model compared with two different inputs. Based on the partial least squares regression method, the estimation of SPAD of Cinnamomum camphora dwarf forest with vegetation index as the model independent variable was slightly better. Based on support vector regression, BP neural network and RBF neural network, the estimation of SPAD of Cinnamomum camphora dwarf forest with band reflectance as the model independent variable was slightly better. Compared with partial least squares regression, support vector regression and BP neural network, the accuracy of Cinnamomum camphora SPAD inversion based on RBF neural network was the highest. Taking the band reflectance and vegetation index as the input of the model as examples, the coefficient of determination (R2) was respectively 0.788 and 0.751, and root mean square error (RMSE) was respectively 1.838 and 2.457, indicating that RBF neural network had obvious advantages in predicting the SPAD of Cinnamomum camphora dwarf forest.