1. An application recommending method, comprising: acquiring a first behavior eigenvalue according to behavior data of a first user; determining at least one second user from a user relationship chain of the first payment relationship chain of the first user, a degree of similarity between a second behavior eigenvalue and the first behavior eigenvalue being greater than a preset threshold, and the second behavior eigenvalue being a behavior eigenvalue of the second user; determining a to-be-recommended application for the first user on a basis of behavior data of the at least one second user; and acquiring a scheduling weight of each compute node before the acquiring a first behavior eigenvalue and computing a system load of the compute node, wherein the system load of the compute node is determined in accordance with: 
 Z i =Ri* 0.8*α+0.2/ Ti*β, wherein Z i indicates a system load of an i th compute node, Ri indicates a resource utilization of the i th compute node, Ti indicates a computation delay of the i th compute node, and α and β are both constants.