Aiming at the problems that traditional centralized computing cannot effectively deal with the huge data generated by massive devices, and the limited resources of MEC server can easily cause resource congestion, and complex communication environment can lead to user task unloading transmission obstruction or even interruption, this paper proposed a method for RIS aided multi-MEC server joint task unloading and resource allocation method. Since the problem of maximizing system unloading utility is a mixed integer nonlinear programming problem, it is difficult to solve directly, so the overall solution process of this problem adopted alternating optimization method and iterative solution. It maximized the system offloading utility through joint optimization of RIS phase shift matrix, MEC server-side CPU computing resources, associated decision-making and clients of uplink transmission power. Specifically, quasi-convex optimization technique and convex optimization techniques respectively solved the RIS best phase shift, user transmission power allocation and MEC server computing resource allocation decisions through the best phase planning, and designed an improved heuristic algorithm to solve the user associated with MEC servers decisions. The simulation results show that the proposed method combining RIS and the improved heuristic improves the average unloading utility of the system by about 22.89% compared with the traditional heuristic algorithm, and the proposed method improves the unloading utility by about 14.02% compared with the classical solution based on local search in the benchmark scheme. Therefore, the proposed method is beneficial to improve the communication service quality of users.