With the acceleration of new infrastructure, cloud computing will be given an entirely new opportunity to develop. Data centers, as the infrastructure of cloud computing, theirs internal servers are continuously updated, which leads to the heterogeneity of computing resources. How to efficiently schedule jobs in heterogeneous cloud environment has become an increasingly popular research. This paper designs a multi-objective reinforcement learning job scheduling method using AHP fixed weight in heterogeneous cloud environment. First, we define the execution time, energy consumption, execution cost, etc. Service delay cost is used to describe the user satisfaction for service. Then, a comprehensive evaluation method for multi-objective scheduling is designed. The weight coefficient of each object is determined by the analytic hierarchy process(AHP). The method is introduced into the calculation of rewards, which can reflect the overall situation and serve as an excellent learning tool for the following jobs. The experimental results show that the proposed method can better optimize the execution efficiency, while ensuring the interests of users and service providers.