Abstract: In intelligent service task, it is difficult for indoor mobile robots to obtain semantic information of complex environment. A semantic map based on semantic acquisition structure of environment is constructed by designing cloud semantic database. The robot can not only get the geometric description of environment, but also obtain the semantic map which contains objects relationship based on rich semantic database of complex environment. It solves the low reliability of adding semantic information, the error of updating map and the lack of scalability in the process of constructing the semantic map. It begins by presenting a semantic database construction project. Then semantic sub-databases are obtained by classifying the semantic database based on SVM (support vector machine) algorithm. On the base of semantic sub-databases, the feature model database is formed by extracting key feature points based on network text classification. By combining the semantic sub-database with the semantic classification list, the objects can be identified. Secondly, the implementation of cloud semantic map for the intelligent service task is discussed. Based on the multi-scale image segmentation and the analysis of disparity map, annotation database and belonging database are designed to describe the belonging relationship between objects. Finally, the semantic map is constructed and the classification efficiency of semantic database is analyzed in simulation experiments to verify the validity of the method.