Soil organic matter (SOM) content is an important indicator of soil quality, which could be predicted using hyper spectral data rapidly. A total of 111 soil samples and hyperspectral data (325~1 075 nm) were collected from the coastal plain on the southern of Laizhou Bay. In previous study, most prediction model is based on the characteristic band and the linear empirical model, ignoring the information redundancy and collinearity between bands, the prediction accuracy is not high and it is difficult to be extended to other regions. In order to maximize the elimination of band information noise and improve the model prediction accuracy,the organic matter content of soil samples was measured as dependent variable. Through principal component analysis (PCA), the measured spectral is reduced to 6 principal components, and water and vegetation spectral characteristic indices are extracted as the independent variables. At last, we analyzed the prediction effect of different model on soil organic matter with MLR and BPN models. The results show: (1) the six principal components extracted from PCA on spectral information could be used to characterize the spectral characteristics of chlorophyll, salt, humic acid, materialized slag and micro- Landform. (2) The prediction accuracy of BPN model based on 6 principal components as independent variable is better than that of MLR model with R2 of 0.704 and 0.643 respectively. After adding water and vegetation spectral characteristic index as an independent variable to prediction models, the prediction accuracy of MLR and BPN increased by 6.1% and 5.2%, and R2 reached 0.712 and 0.764, respectively; (3) BPN model based spectral principal components and spectral characteristic indices as independent variables could predict soil organic matter with the highest accuracy, which has a potential application in soil organic matter prediction and mapping.