Shared bikes represent a crucial component of urban transportation. The randomness of user demand for shared bikes with fixed piles leads to unbalanced demand in time and space, and even the difficulty in renting a bike, which cannot meet the user demand during peak hours. Therefore, high-frequency users frequently travel to nearby stations to rent a bike for serving, which means that there are implicit demands. To ascertain the implicit needs of the station and to determine the critical status of the reference station for judging the traveling condition of the users in the neighboring station is also mined, and the implicit demand of the station is also obtained based on the change of the state map of the station and the demand determination model. Then, according to the real demand of the station, the Long Short-Term Memory and regional scheduling model based on the real demand are established. The model aims to identify the optimal path with the lowest scheduling cost, which is determined through a genetic algorithm. This approach provides a reference for balanced scheduling based on real demand. The results demonstrate that, when transportation costs are similar, the scheduling method under real demand can alleviate the problem of users' difficulty in renting a bike, thereby reducing the loss of high-frequency users.