The dynamic and uncertainty of cloud resource makes resource allocation and task scheduling more difficult. In order to retrieve accurate resource information about dynamic loads and available capacity, this study proposes a resource evaluation model based on entropy optimization and dynamic weighting. The entropy optimization filters the resources that satisfy user QoS and system maximization by goal function and constraints of maximum entropy and the entropy increase principle, which achieves optimal scheduling and satisfied user QoS. Then the evaluation model evaluates the load of having filtered resources by dynamic weighted algorithm. In order to reduce energy consumption, achieve load balancing and improve system utilization, the study allows the migration or release the resources which overload and unavailable for a long time. Experimental results show the effect of entropy optimization on user QoS and system maximization, and dynamic weighted algorithm benefits load balancing and system utilization. The experimental results prove that the evaluation model achieves multi-objective optimization such as satisfying user QOS, reducing energy assumption, balancing load, improving system utilization and so on.