The study on the granularity of marine sediments is helpful in understanding the impact of human activities on the natural marine environment. The fusion of principal component analysis and successive projection algorithm combines the advantages of both spectral feature extraction methods. It can obtain richer feature wavelengths than a single feature extraction method, achieve rejection of irrelevant features and interference information, minimize the loss of feature information, and facilitate the analysis of sediment grain size. In this paper, 32 sediments from the surface layer of the intertidal zone of East Dayang Village in Qingdao City were divided into four sediment samples with different grain sizes of 0.3～0.2, 0.2～0.1, 0.1～0.075 and <0.075 mm. The visible-NIR reflectance spectra of 32 sediments with different grain sizes were measured separately, with 128 spectra samples. The 128 spectral samples were divided into modeling set and test set in the 2∶1, 1∶1 and 1∶2 ratio for analysis. An algorithm fused with principal component analysis and successive projection algorithm was used to extract the characteristic spectra of different grain-size sediments, and the support vector machine algorithm was used to build a grain-size classification model. The results show that the fusion algorithm test set correct rates of 83.33%, 82.81%, and 75.29% at 2∶1, 1∶1 and 1∶2, respectively. All the correct rates were significantly improved relative to the single feature extraction algorithm, except for the lower than 90.47% correct rate for the test set of the continuous projection algorithm at the 2∶1 ratio, indicating that the classification models were built by using the extracted feature spectra of the fusion algorithm. The classification model using the fused algorithm with the extracted feature spectra has an advantage over the model using two separate feature extraction algorithms under the condition of a small training set and clear particle size. Adopting a classification model a marine sediment particle size based on principal component analysis and continuous projection fusion algorithm can improve the correct classification rate results of marine sediment particle size, establish a particle size classification model with a higher correct rate, and provide a solution for fast particle size classification.