In recent years, spatial crowdsourcing platforms attract more and more attention. One of the core issues is to assign proper workers to users to finish their tasks under the temporal and spatial constraints. Most existing works aim to maximize the number of tasks that are finished or the sum of utility score. These approaches ignore the preference of users and workers. Moreover, existing works usually only focus on two roles, workers and users. Workers travel to the location of users to finish the tasks. However, new spatial crowdsourcing platforms contain three types of roles, workers, users, and workplaces. The platforms assign workplaces for workers and users to finish the tasks. Thus, the stable matching problem in the three-dimensional platforms is proposed to solve the static scenarios. However, most spatial crowdsourcing platforms are online scenarios. Workers and tasks issued by the users appear in real time. Therefore, a three-dimensional online stable matching problem is formalized in new spatial crowdsourcing platforms. A baseline algorithm and an improved algorithm are proposed which benefit from the advantages of artificial intelligence to solve this problem. Finally, extensive experiments are conducted on real datasets and synthetic datasets to verify the efficiency and effectiveness of the proposed algorithms.