In order to establish a high-precision detection model of moisture content in single maize seed, totally 80 maize seed samples with different moisture content were prepared. Hyperspectral reflection image acquisition was carried out for maize embryo up and embryo down respectively. Totally 100 grains were sampled for each sample, and the wavelength range was 968.05~2575.05nm. PCA was used to quickly extract the spectrum of a single seed. After multiple scattering correction pretreatment, the random forest (RF) and AdaBoost algorithm were used to establish the moisture content detection model of a single seed, and the characteristics of the two algorithms were integrated. An improved RF based on weighting strategy was proposed to model the moisture content of a single seed. The improved RF model was established by using the upward spectral information of single maize seed embryo. The correlation coefficient R of the training set was 0.969, the root mean square error RMSEC of the training set was 0.094%, the test set R was 0.881, and the root mean square error RMSEP of the test set was 0.404%. The improved RF model was established by using the downward spectral information of single maize seed embryo. The training set R was 0.966, RMSEC was 0.100%, the test set R was 0.793 and RMSEP was 0.544%. The experimental results showed that the generalization ability and prediction accuracy of the improved RF were significantly better than that of RF and AdaBoost algorithms. The moisture content detection model of single maize seed with seed embryo upward was better than that with seed embryo downward. The maize seed moisture detection model established by hyperspectral detection technology combined with integrated learning algorithm had high prediction accuracy and good robustness.