The invention discloses a method for identifying characteristic land categories of ocean remote sensing images of a coast on the basis of semi-supervised learning and belongs to the field of identification of semi-automatic remote sensing images. The method comprises the following steps of: selecting a marking sample for each type of characteristic ground objects; constructing a dividing result facing to the remote sensing images of an object; computing an initial estimation value of probability that pixels of all samples are subordinate to the characteristic land categories and computing theprobability that sample data falls under components of the characteristic land categories; amending a probability image by using a characteristic space rule; judging the characteristic land categories which the remote sensing images belong to, realizing the identification of the characteristic land categories and outputting an identification result drawing. The method is combined with the priori knowledge and the statistical property of the data and can guide the data mining process by the topographical priori knowledge. Practice proves that the algorithm can effectively carry out classification of the remote sensing images to obtain a satisfying result, has the characteristics of high efficiency and high accuracy and can be directly used for maintaining and updating remote sensing thematic information of all levels of fundamental geographic information databases in China.