Dynamic language can take advantage of the profiling information at runtime to guide various optimizations of the program. However, the existing JAVA virtual machine does not effectively utilize the information collected at runtime, and directly discards it at the end. It re-monitors and collects the information needed for optimization when the program is executed again. We therefore propose a static and dynamic adaptive optimization method based on HotSpot virtual machine, which saves the optimal parameters or information obtained by the optimized object iterative search at runtime into the resource library. It can learn from the resource library to obtain the best parameters or options suitable for the current program, and effectively use the data accumulated at runtime. Resource analysis is static and offline, and does not take up the overhead for running the application. In the process of iterative learning, the accuracy and efficiency of the resource library learning process are ensured by avoiding redundancy instances to enter the library and removing noise instances from the library. Experiments show that the proposal is practical in guiding the adaptive optimization for Java virtual machine on different platforms.