The Social Internet of Things(SIoT) introduces the concept of social relations to objects, so as to realize the discovery of objects and services, the interaction between objects, and the connection of goods network. The cloud-services in SIoT which are capable of perceiving real environments, can combine with indoor positioning technologies based on Wireless Networking Technologies to provide convenient services to users. However, indoor positioning algorithms using the received signal strength indicator (RSSI) are vulnerable to environmental interference, and the positioning results are unstable. This paper proposes an indoor positioning technology based on multidimensional spatial similarity. In particular, the fingerprint node database is optimizing by the Machine Learning(ML) mechanism in the cloud. The fingerprint nodes are screened by multidimensional spatial similarity. Moreover, the distance among the nodes is used as a weight factor to improve the traditional triangle localization algorithm. Furthermore, the weighted median Gaussian filter which can reduce the adverse effects of noise on the positioning accuracy is used to improve the efficiency of target positioning. Finally, tests are conducted in a laboratory environment. The results show that the accuracy of our method is higher than that of traditional indoor positioning methods, while the average positioning error is reduced significantly. In summary, our method has stronger anti-interference ability and meets the requirements of indoor positioning.