A classification method of oilseed rape and weeds based on hyperspectral information was put forward. Standard normal variate transformation (SNV), de-trending, multiplicative scatter correction (MSC), moving average (MA), savitzky-golay smoothing(SG), baseline and normalize were applied to data preprocess. Principal component analysis loadings (PCA loadings), x-loading weights, regression coefficient (RC) and successive projection algorithm (SPA) were used to extract feature wavelengths. Partial least-squares discriminant analysis (PLS-DA), extreme learning machine (ELM) and support vector machine(SVM) were employed to establish classification models. The overall results shows that the ELM models with the selected wavelengths of PCA loadings, x-loading weights and SPA based on de-trending preprocessed spectra has obtained the best results, with 100% classification accuracy for both the calibration set and the prediction set. The index of average classification accuracy is introduced to evaluate classification models accuracy under different experimental time. The results indicates that it is feasible to use near-infrared hyperspectral imaging to identify the oilseed rape and weeds.