It is expected that multiple virtual power plants (multi-VPPs) will join and participate in the future local energy market (LEM). The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator (LEMO) and each VPP. We propose a bounded rationality-based trading model of multi-VPPs in the local energy market by using a dynamic game approach with different trading targets. Three types of power bidding models for VPPs are first set up with different trading targets. In the dynamic game process, VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents' bidding strategies and its own clustered resources. LEMO would decide the electricity buying/selling price in the LEM. Furthermore, the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization (IPSO) algorithm and conventional large-scale optimization. Finally, case studies are conducted to show the performance of the proposed model and solution approach, which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.