As-is building information model (BIM) is regarded as the mainstream solution of the digital twin (DT) for the intelligent building management, especially in the facilities management (FM) phase for the existing buildings. The current automatic scan-to-BIM methods mainly focus on the detailed geometric information modelling. However, the attributes information of the ‘secondary’ building objects is equally valuable comparing to that of the primary structural objects in the FM workflow. These components may include the light fixture, plumbing and heating terminal and furniture. The knowledge supporting to the FM practice can be extracted based on the geometric and attribute information. Therefore, the ‘secondary’ building components should be efficiently modelled as the main operation and maintenance targets in the FM phase. This paper proposes an automatic ‘secondary’ object-based BIM model retrieval method based on segmented point cloud model. The machine learning (ML) supported technical conceptual framework will be introduced in this paper.