Visible-near infrared spectroscopy has been shown to be a fast and effective tool for organic carbon (TOC) content prediction. However, the research target of using spectral prediction of TOC content is mainly soil or lake sediment, and there is little research on marine sediments in intertidal zone. In order to predict the content of TOC in intertidal sediments quickly and accurately, this study constructed TOC prediction model by combining abnormal sample elimination, spectral feature transformation and feature wavelength extraction, that is, collecting sediment spectra of samples in intertidal zone, using Markov distance, standard lever value and student residuals combined analysis method to remove abnormal samples, using multivariate scattering correction (MSC), smoothing + differential for spectral transformation, using genetic algorithm (GA) to extract characteristic wavelengths, using partial least squares method (PLS), least squares support vector machine (LSSVM) and BP Neural Network (BPNN) to model and predict sediment TOC content, using the decision coefficient (R2) and residual estimation deviation (PRD) to evaluate model accuracy. The results showed that the elimination of abnormal samples improved model accuracy, and the test R2 and PRD of the BPNN model increased by 28% and 39% respectively. The effect of MSC was better than that of smoothing+differential, and the test R2and PRD of PLS, LSSVM and BPNN models based on MSC were 0.81, 0.86, 0.78 and 2.25, 2.59, 2.07, respectively, which enhanced 9%~20% (R2) and 11%~22% (PRD) than that based on smoothing+differential, suggesting that MSC has a strong ability to extract TOC information. GA is not conducive to increasing model accuracy, the test R2 and PRD of models based on GA reducedby 9%~36% and 18%~33%, respectively. This may be related to the low number of characteristic wavelengths extracted by GA. The BPNN model has the lowest predictive accuracy and may be related to its vulnerability to local minimums. PLS model has high accuracy and can predict TOC content in intertidal zone. Basing on abnormal sample elimination and MSC, the modeling set R2 of PLS model was 0.98, and the prediction set R2 and RPD were 0.81 and 2.25 respectively. The accuracy of LSSVM model was better than that of PLS, the modeling set R2 was 0.99, the test set R2 and RPD were 0.86 and 2.59 respectively, implying excellent TOC quantitative prediction ability of LSSVM. In a word, for the prediction of TOC content in intertidal sediments, the combination of abnormal sample elimination, MSC spectral transformation and LSSVM modeling can obtain a reliable and stable prediction model.