To address the dispatching problem of bike?sharing， considering the budget constraints， user maximum walking distance restrictions， user temporal and spatial demands and dynamic changes in the distribution of shared bicycles， a bike?sharing dispatching strategy with user incentives was proposed to improve the long?term user service rate of the bike?sharing platform. The dispatching strategy consists of a task generation algorithm， a budget allocation algorithm and a task allocation algorithm. In the task generation algorithm， the Long Short?Term Memory （LSTM） network was used to predict the future bike demand of users； in the budget allocation algorithm， the Deep Deterministic Policy Gradient （DDPG） algorithm was used to design a budget allocation strategy； after the budget was allocated to the tasks， the tasks needed to be allocated to the user for execution， so a greedy matching strategy was used for task allocation. Experiments were carried out on the Mobike dataset to compare the proposed strategy with the dispatching strategy with unlimited budget （that is， the platform is not limited by budget and can use any money to encourage users to ride to the target area）， the greedy dispatching strategy， the dispatching strategy with truck hauling， and the situation without dispatching. Experimental results show that the proposed dispatching strategy with user incentive can effectively improve the service rate in the bike?sharing system compared to the greedy dispatching strategy and dispatching strategy with truck hauling.