Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities, especially in the new era of 5G and Internet-of-Things (IoT) environments. A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems. In the real world, the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage. In addition to pure manual rebalancing, in several works, attempts were made to predict the demand for bikes. In this paper, we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion. We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering. We carefully analyzed and discovered the group of features that impact the demand of bikes, from historical bike-sharing records and 5G IoT environment data. We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand. We performed sufficient experiments on two real-world datasets. The results confirm that compared to some existing methods, our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.