Bike-sharing system is becoming more and more popular and there accumulates a large volume of trajectory data. In the bike-sharing system, the borrowing and returning behavior of users are arbitrary. In addition, bike-sharing system will be affected by weather, time period, and other dynamic factors, which makes shared bike scheduling unbalanced, affects user’s experience, and causes huge economic losses to operators. A novel shared-bike demand prediction model based on station clustering is proposed, the activeness of stations is calculated by constructing a bike transformation network. The geographical location of stations and the bike transmission patterns are taken into full consideration, and the stations with near distances and transformation patterns are aggregated into a cluster based on the idea of data field clustering. In addition, a method for computing the optimal number of cluster centers is presented. The influence of time and weather factors on bike demand is fully analyzed and the Pearson correlation coefficient is used to choose the most relevant weather features from the real weather data and transformed into a three-dimensional vector by taking into consideration the historical demand for bicycles in the cluster. In addition, long short-term memory (LSTM) neural network with multiple features is employed to learn and train the feature information in the vector, and the bike demand in each cluster is predicted and analyzed every thirty minutes. When compared with the traditional machine learning algorithms and the state-of-the-art methods, the results show that the prediction performance of the proposed model has been significantly improved.