In Low Earth orbit （LEO）satellite multi-beam communication scenario， the traditional fixed resource allocation algorithm can not meet the differences in channel capacity requirements of different users. In order to meet the requirements of users， the optimization model of minimum supply-demand difference of combining channel allocation， bandwidth allocation and power allocation was established， and Pattern Division Multiple Access technology （PDMA）was introduced to improve the utilization of channel resources. In view of the non-convex characteristic of the model， the optimal resource allocation strategy learned by the Q-learning algorithm was used to allocate the channel capacity suitable for each user， and a reward threshold was introduced to further improve the algorithm， speeding up the convergence and minimizing the difference between supply and demand when the algorithm converged. The simulation results show that the convergence speed of the improved algorithm is about 3.33 times that before improvement； the improved algorithm can meet larger user requirement， about 14% higher than the Q-learning algorithm before improvement， about 2.14 times that of the traditional fixed algorithm.