Multi-beam Backscatter Strength (BS) data can be used to identify the types of the seafloor and the distribution of seafloor geological hazards, but the traditional method for BS data interpretation is time-consuming and subjective. We applied K-S test techniques to automatically interpret the multibeam BS data. After eliminating the effect of incidence angle, our statistics analysis show that four typical sediments have the Gaussian distributions, and there is a good correlation between BS and the grain size of seafloor sediments. Based on the distribution trends, five other BS distributions were constructed the missing typical seafloor without seafloor samplings. Then, we performed a single sample K-S test method to classify the seafloor sediments for the whole surveyed region. The unknown types of seafloor sediments can be judged and classified by compared their BS distribution with the known typical Gaussian distributions and measuring the similarity between the two. Through experimental comparisons, we determined the optimal window size for experiments on this area to be 30 m × 30 m. At the same time, we set the classification confidence level to 90%, and we obtained results from our experiments that the overall recognition rate (the ratio of the identified area to the total area) reached 92%, and the classification results also matched all the sampling results with high classification accuracy, and the method achieved good results. The results illustrate our automatic method can replace the conventional in-house BS interpretations and reduce offshore operation costs. It requires only a small amount of seafloor samples to achieve automatic seabed classification for the entire area. In addition, the reliability of the classification can be evaluated by a parameter of statistics analysis. The high accuracy of the classification results of this method is particularly suitable for areas where large areas of typical substrate are distributed.